$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning | Paper Citation Record

Observation 41bee4ec-4ed7-40df-aa3e-b4b85101c86b · outbound

This paper cites Facenet: A unified embedding for face recognition and clustering.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Facenet: A unified embedding for face recognition and clustering

Reference 1

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This paper cites Sphereface: Deep hypersphere embedding for face recognition.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Sphereface: Deep hypersphere embedding for face recognition

Reference 2

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Observation e91f0eb6-bb4b-4e40-8e1a-49a828a9ebf6 · outbound

This paper cites Arcface: Additive angular margin loss for deep face recognition.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Arcface: Additive angular margin loss for deep face recognition

Reference 3

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Observation 7f866a85-c9be-421a-9da1-e85cd62ac788 · outbound

This paper cites Netvlad: Cnn architecture for weakly supervised place recognition.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Netvlad: Cnn architecture for weakly supervised place recognition

Reference 4

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This paper cites Unifying deep local and global features for image search.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Unifying deep local and global features for image search

Reference 5

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Observation c0d47a2c-e168-42ca-93ce-07dee784fd5b · outbound

This paper cites Patch-netvlad: Multi-scale fusion of locally-global descriptors for place recognition.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Patch-netvlad: Multi-scale fusion of locally-global descriptors for place recognition

Reference 6

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Observation 98ef0fa0-9456-4138-a029-dff5a7c06fe0 · outbound

This paper cites Large-scale image retrieval with attentive deep local features.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Large-scale image retrieval with attentive deep local features

Reference 7

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Observation d0c7216e-b012-4d6c-bbb0-c3b5ceff9f3d · outbound

This paper cites Instance-level image retrieval using reranking transform- ers.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Instance-level image retrieval using reranking transform- ers

Reference 8

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Observation 169bfad0-44e2-492c-8d1f-d1d9851c99b6 · outbound

This paper cites Universal instance perception as object discovery and retrieval.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Universal instance perception as object discovery and retrieval

Reference 9

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Observation 5fc6b16d-3ce9-4c16-afec-4c45657c2357 · outbound

This paper cites Building machine learning models like open source software.Commun.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Building machine learning models like open source software.Commun

Reference 10

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Observation fd819b52-3718-44cb-b0f9-972b021908ce · outbound

This paper cites A survey on model moerging: Recycling and routing among specialized experts for collaborative learning.Trans.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning A survey on model moerging: Recycling and routing among specialized experts for collaborative learning.Trans

Reference 11

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Observation f4f309c1-d9cf-40c0-b838-44eab0354ac3 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning LLaMA: Open and Efficient Foundation Language Models

Reference 12

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Observation f9b02abd-7e06-48af-9584-31c5ea9afdef · outbound

This paper cites Stationary representations: Optimally approximating compatibility and implications for improved model replacements.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Stationary representations: Optimally approximating compatibility and implications for improved model replacements

Reference 13

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Observation 873b1f36-7829-456c-bfc9-1daca9d12f5e · outbound

This paper cites MUSCLE: A model update strategy for compatible LLM evolution.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning MUSCLE: A model update strategy for compatible LLM evolution

Reference 14

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Observation ae29f1f6-e166-4af9-b98f-ce3fc0d513b5 · outbound

This paper cites Towards backward-compatible representation learning.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Towards backward-compatible representation learning

Reference 15

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Observation b56dfc32-649f-4751-9b2b-85a698d9d234 · outbound

This paper cites an unresolved cited work.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Unresolved cited work

Reference 16

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Observation 0cb105ab-54b9-43e0-ad3e-4e1ace1a6989 · outbound

This paper cites Positive-congruent training: Towards regression-free model updates.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Positive-congruent training: Towards regression-free model updates

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:24:24.136738Z

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Observation 993f5eeb-c1b9-4a1b-b6e1-c441ec4d4f22 · outbound

This paper cites Cores: Compatible represen- tations via stationarity.IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1–16.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Cores: Compatible represen- tations via stationarity.IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1–16

Reference 18

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Observation 8c6126c8-684f-4879-b147-d77ab7a77f0f · outbound

This paper cites Model soups: aver- aging weights of multiple fine-tuned models improves accuracy without increasing inference time.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Model soups: aver- aging weights of multiple fine-tuned models improves accuracy without increasing inference time

Reference 19

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Observation 2599cd06-2561-430a-b518-c6f11d2d3a9c · outbound

This paper cites Towards universal backward-compatible representation learning.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Towards universal backward-compatible representation learning

Reference 20

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Observation 9a14676e-b6fe-4e8e-b037-7ef252ec79e0 · outbound

This paper cites Learning compatible embeddings.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Learning compatible embeddings

Reference 21

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Observation e081d7ea-13fb-43f4-9ce8-8fc4a698a93b · outbound

This paper cites Fastfill: Efficient compatible model update.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Fastfill: Efficient compatible model update

Reference 22

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Observation 7436ba28-526f-4e87-80d8-2f151badf810 · outbound

This paper cites Btˆ 2: Backward-compatible training with basis transformation.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Btˆ 2: Backward-compatible training with basis transformation

Reference 23

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Observation 61e1f127-e04b-4f99-8e34-f7a379ccfaf9 · outbound

This paper cites Backward-compatible aligned representations via an orthogonal transformation layer.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Backward-compatible aligned representations via an orthogonal transformation layer

Reference 24

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Observation 8ca305d0-992e-4aa1-ae5c-d3e9e7b1216f · outbound

This paper cites For- ward compatible training for large-scale embedding retrieval systems.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning For- ward compatible training for large-scale embedding retrieval systems

Reference 25

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Observation e380a0fc-88a9-4211-8458-ab9c3164f1a2 · outbound

This paper cites Testing the manifold hypothesis.Journal of the American Mathematical Society, 29(4):983–1049.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Testing the manifold hypothesis.Journal of the American Mathematical Society, 29(4):983–1049

Reference 26

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Observation 404ff4b0-ee95-44c2-bf1c-adab71609dca · outbound

This paper cites Position: The platonic representation hypothesis.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Position: The platonic representation hypothesis

Reference 27

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Observation d0350205-1dac-4406-9b04-3c170a83970f · outbound

This paper cites Latent space translation via semantic alignment.Advances in Neural Information Processing Systems, 36.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Latent space translation via semantic alignment.Advances in Neural Information Processing Systems, 36

Reference 28

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Observation b4b2bde5-d12c-4d43-a328-217a8e64e542 · outbound

This paper cites Latent functional maps: a spectral framework for representation alignment.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Latent functional maps: a spectral framework for representation alignment

Reference 29

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Observation 71154492-3bf5-48e4-8546-6d52a2368e12 · outbound

This paper cites Relative representations enable zero-shot latent space communication.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Relative representations enable zero-shot latent space communication

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:24:24.188229Z

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Observation 1be9cb5c-49a5-44fa-bba2-db3387adedb2 · outbound

This paper cites Latent Space Translation via Inverse Relative Projection.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Latent Space Translation via Inverse Relative Projection

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-21T22:24:23.610201Z

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Observation 14b2a90f-eed5-446f-90fd-53f59c62ec81 · outbound

This paper cites The stability-plasticity dilemma: Investigating the continuum from catastrophic forgetting to age-limited learning effects.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning The stability-plasticity dilemma: Investigating the continuum from catastrophic forgetting to age-limited learning effects

Reference 32

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Observation ee0b4548-d562-4a2c-9458-00447d49c63c · outbound

This paper cites Towards better plasticity-stability trade-off in incremental learning: A simple linear connector.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Towards better plasticity-stability trade-off in incremental learning: A simple linear connector

Reference 33

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Observation 863970a1-215b-4473-aaf5-3a48a4e4b918 · outbound

This paper cites On the stability-plasticity dilemma of class-incremental learning.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning On the stability-plasticity dilemma of class-incremental learning

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:24:24.180757Z

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Observation f7187634-1cfc-4016-b7e2-e8dc917f54a6 · outbound

This paper cites Generalized clustering and multi-manifold learning with geometric structure preservation.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Generalized clustering and multi-manifold learning with geometric structure preservation

Reference 35

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Observation 99bd5e9f-9918-4d04-a382-2825206a8651 · outbound

This paper cites Can we gain more from orthogonality regularizations in training deep networks?Advances in Neural Information Processing Systems, 31.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Can we gain more from orthogonality regularizations in training deep networks?Advances in Neural Information Processing Systems, 31

Reference 36

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Observation d1b4aedc-b7b1-4c79-9bf8-b6adbde9ac35 · outbound

This paper cites Hot-refresh model upgrades with regression-free compatible training in image retrieval.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Hot-refresh model upgrades with regression-free compatible training in image retrieval

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:24:24.239890Z

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Observation b2f7d8b9-5799-40f2-8980-9972470fea1e · outbound

This paper cites Boundary-aware backward-compatible representation via adversarial learning in image retrieval.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Boundary-aware backward-compatible representation via adversarial learning in image retrieval

Reference 38

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Observation 1aaa0772-6aeb-445f-98f7-2f88ee4b5d15 · outbound

This paper cites Asymmetric metric learning for knowledge transfer.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Asymmetric metric learning for knowledge transfer

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:24:24.158064Z

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Observation 743e6c83-dd7c-463b-9084-1830f3783d85 · outbound

This paper cites Cl2r: Compati- ble lifelong learning representations.ACM Transactions on Multimedia Computing, Communications and Applications, 18(2s):1–22.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Cl2r: Compati- ble lifelong learning representations.ACM Transactions on Multimedia Computing, Communications and Applications, 18(2s):1–22

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:24:24.153571Z

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Observation e8b0b3c8-7eaf-45c3-b87f-d4e4833e6e5e · outbound

This paper cites Memory-efficient incremental learning through feature adaptation.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Memory-efficient incremental learning through feature adaptation

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:24:24.168340Z

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Observation b33d2875-af41-433f-b8a5-137ae184ef56 · outbound

This paper cites Unified representation learning for cross model compatibility.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Unified representation learning for cross model compatibility

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:24:24.163408Z

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Observation f62e655d-43ff-47ce-98e3-6fdb3d328953 · outbound

This paper cites Privacy-Preserving Model Upgrades with Bidirectional Compatible Training in Image Retrieval.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Privacy-Preserving Model Upgrades with Bidirectional Compatible Training in Image Retrieval

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-21T22:24:23.613834Z

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Observation 890ddee0-49fb-4e1f-994c-b040b210ba9a · outbound

This paper cites Manifold alignment using procrustes analysis.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Manifold alignment using procrustes analysis

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:24:24.160198Z

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Observation e8539a93-390f-4c39-bfb0-5282fe29dc84 · outbound

This paper cites Cheap orthogonal constraints in neural networks: A simple parametrization of the orthogonal and unitary group.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Cheap orthogonal constraints in neural networks: A simple parametrization of the orthogonal and unitary group

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:24:24.134342Z

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Observation ca7f5ba5-45c7-4dab-a09d-6f400a9e8e0b · outbound

This paper cites Overcoming catastrophic forgetting in neural networks.Proceedings of the national academy of sciences, 114(13):3521–3526.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Overcoming catastrophic forgetting in neural networks.Proceedings of the national academy of sciences, 114(13):3521–3526

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:24:24.251497Z

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Observation f5599f94-3153-4d89-b973-cbcc86a54a22 · outbound

This paper cites Measuring catastrophic forgetting in neural networks.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Measuring catastrophic forgetting in neural networks

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:24:24.211493Z

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Observation 09a837fc-5bbd-4c31-8711-e403d99e85dc · outbound

This paper cites Generalized BackPropagation, \'{E}tude De Cas: Orthogonality.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Generalized BackPropagation, \'{E}tude De Cas: Orthogonality

Reference 48

Resolution: verified exact; local_arxiv, observed 2026-05-21T22:24:23.603749Z

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Observation fc8b277c-7e2f-48a7-a2a7-51828d040ef1 · outbound

This paper cites Optimization on Submanifolds of Convolution Kernels in CNNs.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Optimization on Submanifolds of Convolution Kernels in CNNs

Reference 49

Resolution: verified exact; local_arxiv, observed 2026-05-21T22:24:23.600749Z

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Observation 94c4bda9-cab5-48df-94bc-87fd931b9682 · outbound

This paper cites Orthogonal weight normalization: Solution to optimization over multiple dependent stiefel manifolds in deep neural networks.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Orthogonal weight normalization: Solution to optimization over multiple dependent stiefel manifolds in deep neural networks

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:24:24.209333Z

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Observation 006aaac2-26d7-4cea-89cc-8f489f5fcec3 · outbound

This paper cites US Government printing office.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning US Government printing office

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:24:24.230393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1b153279-a0ef-46ec-be99-b1461f98cd34 · outbound

This paper cites Activation functions in neural networks.Towards Data Sci, 6(12):310–316.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Activation functions in neural networks.Towards Data Sci, 6(12):310–316

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:24:24.206930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation feec5784-f08d-4ee7-8ac5-eb6d639d62e9 · outbound

This paper cites On the approximation of the step function by some sigmoid functions.Mathematics and Computers in Simulation, 133:223–234.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning On the approximation of the step function by some sigmoid functions.Mathematics and Computers in Simulation, 133:223–234

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:24:24.196576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2c8bed71-4db8-42c4-bbe0-7c8b12783a08 · outbound

This paper cites Stablerep: Synthetic images from text-to-image models make strong visual representation learners.Advances in Neural Information Processing Systems, 36.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Stablerep: Synthetic images from text-to-image models make strong visual representation learners.Advances in Neural Information Processing Systems, 36

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:24:24.201789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 735c575f-8f07-4a7b-9108-2fdba9465547 · outbound

This paper cites Hierarchy-based image embeddings for semantic image retrieval.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Hierarchy-based image embeddings for semantic image retrieval

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:24:24.216328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation bc88288b-762b-4ecd-9ce3-5bc1c1d38536 · outbound

This paper cites On the unreasonable effectiveness of centroids in image retrieval.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning On the unreasonable effectiveness of centroids in image retrieval

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:24:24.248754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 58125679-028c-4616-8ff2-d1a83c5f9309 · outbound

This paper cites Imagenet large scale visual recognition challenge.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Imagenet large scale visual recognition challenge

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:24:24.232464Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 745882db-3f5c-42c8-9afa-f93bdb0a77dc · outbound

This paper cites Krizhevsky.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Krizhevsky

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:24:24.234683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 618f6c98-1856-47af-a0c4-466f5b30ff35 · outbound

This paper cites The caltech-ucsd birds-200-2011 dataset.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning The caltech-ucsd birds-200-2011 dataset

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:24:24.241993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4cec9423-7a40-4d0d-b32f-19c1e2b80af1 · outbound

This paper cites Places: A 10 million image database for scene recognition.IEEE Transactions on Pattern Analysis and Machine Intelligence.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Places: A 10 million image database for scene recognition.IEEE Transactions on Pattern Analysis and Machine Intelligence

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:24:24.237652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6d837a66-7d44-47a8-9874-7d74caa3ebf8 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning An image is worth 16x16 words: Transformers for image recognition at scale

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:24:24.218514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a1200795-8900-427e-afb2-4c84fc040446 · outbound

This paper cites Learning transferable visual models from natural language supervision.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Learning transferable visual models from natural language supervision

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:24:24.258561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 32d63d4a-6ef1-4724-8718-eb1a87792d59 · outbound

This paper cites Dinov2: Learning robust visual features without supervision.Transactions on Machine Learning Research.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Dinov2: Learning robust visual features without supervision.Transactions on Machine Learning Research

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:24:24.254216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation bee9e4ec-773f-492a-b76c-198a9927ec41 · outbound

This paper cites Automated flower classification over a large number of classes.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Automated flower classification over a large number of classes

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:24:24.256479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 96a69864-9563-44c0-8f51-29a078adf328 · outbound

This paper cites Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:24:24.228140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a9d917ff-4ee5-4468-b294-3613f39363d8 · outbound

This paper cites Bagdanov.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Bagdanov

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:24:24.223324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1ae5ca25-5671-4751-8f9e-c8e1db1c5dc1 · outbound

This paper cites C-clip: Multimodal continual learning for vision- language model.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning C-clip: Multimodal continual learning for vision- language model

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:24:24.221334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 036cfc5b-b39b-4d82-a426-abd834a3b825 · outbound

This paper cites Scaling Laws for Neural Language Models.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Scaling Laws for Neural Language Models

Reference 68

Resolution: verified exact; local_arxiv, observed 2026-05-21T22:24:23.604124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 004a7b39-aa65-4b6f-9966-370ec25692f6 · outbound

This paper cites Deep double descent: Where bigger models and more data hurt.Journal of Statistical Mechanics: Theory and Experiment, 2021(12):124003.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Deep double descent: Where bigger models and more data hurt.Journal of Statistical Mechanics: Theory and Experiment, 2021(12):124003

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:24:24.213840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5ee6fbdc-6f02-4326-ab15-8535e8be67b5 · outbound

This paper cites Scaling laws for the out- of-distribution generalization of image classifiers.ICML 2021 Workshop on Uncertainty and Robustness in Deep Learning.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Scaling laws for the out- of-distribution generalization of image classifiers.ICML 2021 Workshop on Uncertainty and Robustness in Deep Learning

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:24:24.244232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9cb62680-44fb-4519-9a64-62a56e014f95 · outbound

This paper cites Broken neural scaling laws.

$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning Broken neural scaling laws

Reference 71

Resolution: verified exact; arxiv_id, observed 2026-05-21T22:24:23.601251Z

Source-reported events for the cited work

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