Paper Citation Record · LEDGER

Active Data Curation Effectively Distills Large-Scale Multimodal Models

As of 21 August 2026, this Paper Citation Record lists 100 of 201 outbound references and 4 inbound Pith citation observations for arXiv:2411.18674.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2411.18674 v2

Coverage vector

measured 100 of 201 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T11:06:36.063524Z

measured 104 of 104 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T19:24:43.561850Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-10T15:49:22.329172Z

Reference resolution

100 of 201 outbound references displayed

verified exact6
verified fuzzy0
unresolved94
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0d4b2e3e-e02d-4a63-b2f2-a11a48148d1a · outbound

This paper cites SemDeDup: Data-efficient learning at web-scale through semantic deduplication.

Active Data Curation Effectively Distills Large-Scale Multimodal Models SemDeDup: Data-efficient learning at web-scale through semantic deduplication

Reference 1

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Observation 4aa42ade-cfc5-474b-b512-bde5dbdd3b2d · outbound

This paper cites Effective pruning of web-scale datasets based on complexity of concept clusters.

Active Data Curation Effectively Distills Large-Scale Multimodal Models Effective pruning of web-scale datasets based on complexity of concept clusters

Reference 2

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Observation 600c9fc3-df29-4f6e-a8b2-5c7bcbcde4f6 · outbound

This paper cites DatologyAI Technical Deep-Dive: Image-Text Data Cura- tion at the Billion-Sample Scale.

Active Data Curation Effectively Distills Large-Scale Multimodal Models DatologyAI Technical Deep-Dive: Image-Text Data Cura- tion at the Billion-Sample Scale

Reference 3

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Observation 20971725-ee0d-435c-a5bd-236acbae733c · outbound

This paper cites Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone.

Active Data Curation Effectively Distills Large-Scale Multimodal Models Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Reference 4

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Observation 34ea6aff-6667-4008-8ef9-834944589322 · outbound

This paper cites On-policy distillation of language models: Learn- ing from self-generated mistakes.

Active Data Curation Effectively Distills Large-Scale Multimodal Models On-policy distillation of language models: Learn- ing from self-generated mistakes

Reference 5

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Observation ab019e29-f077-408d-8143-e7f11ca58b8e · outbound

This paper cites Robust cross-modal representation learning with progressive self- distillation.

Active Data Curation Effectively Distills Large-Scale Multimodal Models Robust cross-modal representation learning with progressive self- distillation

Reference 6

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Observation 927fdc49-bb1f-422c-9ed1-1cc31aa78f94 · outbound

This paper cites Do deep nets really need to be deep? Advances in neural information processing systems, 27, 2014.

Active Data Curation Effectively Distills Large-Scale Multimodal Models Do deep nets really need to be deep? Advances in neural information processing systems, 27, 2014

Reference 7

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Observation 9d0e3a35-02c7-40a4-84f3-1505cd740e52 · outbound

This paper cites Objectnet: A large-scale bias-controlled dataset for pushing the limits of object recognition models.

Active Data Curation Effectively Distills Large-Scale Multimodal Models Objectnet: A large-scale bias-controlled dataset for pushing the limits of object recognition models

Reference 8

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Observation 1e23292b-779f-48e7-96cd-cf530d6e6d06 · outbound

This paper cites Robust Active Distillation.

Active Data Curation Effectively Distills Large-Scale Multimodal Models Robust Active Distillation

Reference 9

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Observation 843b4661-1835-4ac2-88ad-27dbbe1d27b3 · outbound

This paper cites The iWildCam 2021 Competition Dataset.

Active Data Curation Effectively Distills Large-Scale Multimodal Models The iWildCam 2021 Competition Dataset

Reference 10

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Observation 82109ff0-6bba-49cf-a742-1a4f9e649733 · outbound

This paper cites Big vision.

Active Data Curation Effectively Distills Large-Scale Multimodal Models Big vision

Reference 11

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Observation 0221f010-2e86-46f7-819c-836590d21044 · outbound

This paper cites Knowledge distillation: A good teacher is patient and consistent.

Active Data Curation Effectively Distills Large-Scale Multimodal Models Knowledge distillation: A good teacher is patient and consistent

Reference 12

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Observation 0851874e-f440-4a09-9fdf-98e78df3eda8 · outbound

This paper cites A Study of Autoregressive Decoders for Multi-Tasking in Computer Vision.

Active Data Curation Effectively Distills Large-Scale Multimodal Models A Study of Autoregressive Decoders for Multi-Tasking in Computer Vision

Reference 13

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Observation 644eca52-7cac-4ce3-8414-a1d42d42bac2 · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

Active Data Curation Effectively Distills Large-Scale Multimodal Models On the Opportunities and Risks of Foundation Models

Reference 14

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Observation 35ece2c4-012e-4fc0-9576-d7c147595e3f · outbound

This paper cites Food-101–mining discriminative components with random forests.

Active Data Curation Effectively Distills Large-Scale Multimodal Models Food-101–mining discriminative components with random forests

Reference 15

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Observation 3402f969-f527-40c9-b58a-2233eea2b06a · outbound

This paper cites JAX: composable transformations of Python+NumPy programs, 2018.

Active Data Curation Effectively Distills Large-Scale Multimodal Models JAX: composable transformations of Python+NumPy programs, 2018

Reference 16

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Observation b6b152f6-1e90-4eb3-9779-e166cca466cc · outbound

This paper cites CoLoR-Filter: Conditional Loss Reduction Filtering for Targeted Language Model Pre-training.

Active Data Curation Effectively Distills Large-Scale Multimodal Models CoLoR-Filter: Conditional Loss Reduction Filtering for Targeted Language Model Pre-training

Reference 17

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Observation e4d53d11-8ef5-43f1-9eb3-ee808d33aa23 · outbound

This paper cites Model compression.

Active Data Curation Effectively Distills Large-Scale Multimodal Models Model compression

Reference 18

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Observation dbb58220-7715-4da3-805e-06d220a3fc5b · outbound

This paper cites Less is More: Removing Text-regions Improves CLIP Training Efficiency and Robustness.

Active Data Curation Effectively Distills Large-Scale Multimodal Models Less is More: Removing Text-regions Improves CLIP Training Efficiency and Robustness

Reference 19

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Observation b58772cd-17b9-49c1-b7e0-e45c9a6ae2d0 · outbound

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

Active Data Curation Effectively Distills Large-Scale Multimodal Models Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts

Reference 20

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Observation 15455433-d252-457c-a4b9-d9901878ecf3 · outbound

This paper cites Distilling knowledge from ensembles of neural networks for speech recognition.

Active Data Curation Effectively Distills Large-Scale Multimodal Models Distilling knowledge from ensembles of neural networks for speech recognition

Reference 21

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Observation f8fd2e5b-089c-458f-80fc-96741edd6bfc · outbound

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Data-free learning of student networks

Reference 22

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Observation 1356620a-65e1-40ab-8779-7da7f059c05e · outbound

This paper cites A simple framework for contrastive learning 9 of visual representations.

Active Data Curation Effectively Distills Large-Scale Multimodal Models A simple framework for contrastive learning 9 of visual representations

Reference 23

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Observation b89bdc8a-d75d-4d31-8b7f-35978bf5cf53 · outbound

This paper cites PaLI: A Jointly-Scaled Multilingual Language-Image Model.

Active Data Curation Effectively Distills Large-Scale Multimodal Models PaLI: A Jointly-Scaled Multilingual Language-Image Model

Reference 24

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Observation 1354a8a6-9b12-440e-8649-230f8146f4c4 · outbound

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Active Data Curation Effectively Distills Large-Scale Multimodal Models On the efficacy of knowledge distillation

Reference 25

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Observation 2ce8da01-b9fe-45ec-8c6a-1174478a2e61 · outbound

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Functional map of the world

Reference 26

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Observation 9566f184-d05f-470c-b55a-23a6c1e575de · outbound

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Cimpoi, S

Reference 27

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Observation 4705e20f-2e17-4333-8f10-613da52954a1 · outbound

This paper cites An analysis of single-layer networks in unsupervised feature learning.

Active Data Curation Effectively Distills Large-Scale Multimodal Models An analysis of single-layer networks in unsupervised feature learning

Reference 28

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Observation 3a181af2-d438-42fb-b13e-31fbf5a86c65 · outbound

This paper cites Teachtext: Crossmodal generalized distillation for text- video retrieval.

Active Data Curation Effectively Distills Large-Scale Multimodal Models Teachtext: Crossmodal generalized distillation for text- video retrieval

Reference 29

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Observation f5267c04-cc29-449f-af25-4df62a02ef38 · outbound

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Imagenet: A large-scale hierarchical image database

Reference 30

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Towards accelerated model training via bayesian data selection

Reference 31

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Observation e5a1b00e-60cb-4d4c-acb5-33dcc77b90f8 · outbound

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Active Data Curation Effectively Distills Large-Scale Multimodal Models BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 32

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Observation 984114c5-7cb2-443d-80b3-028317d67b69 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Active Data Curation Effectively Distills Large-Scale Multimodal Models An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 33

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Observation fa8eacf1-7ea4-46a3-b955-5787c79b5656 · outbound

This paper cites Bad Students Make Great Teachers: Active Learning Accelerates Large-Scale Visual Understanding.

Active Data Curation Effectively Distills Large-Scale Multimodal Models Bad Students Make Great Teachers: Active Learning Accelerates Large-Scale Visual Understanding

Reference 34

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Observation 521243c6-09d7-4743-a389-4f94716c07d7 · outbound

This paper cites Data curation via joint example selection further accelerates multimodal learning.

Active Data Curation Effectively Distills Large-Scale Multimodal Models Data curation via joint example selection further accelerates multimodal learning

Reference 35

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Everingham, L

Reference 36

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Reinforce data, multiply impact: Improved model accuracy and robustness with dataset reinforcement

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Improving clip training with language rewrites

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Irreducible Curriculum for Language Model Pretraining

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Neural data filter for bootstrapping stochastic gradient descent

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Data determines distributional robustness in contrastive language image pre-training (clip)

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Data Filtering Networks

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Mosaicking to distill: Knowledge distillation from out-of-domain data

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Compressing visual- linguistic model via knowledge distillation

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Active Data Curation Effectively Distills Large-Scale Multimodal Models SEED: Self-supervised Distillation For Visual Representation

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Does learning require memorization? a short tale about a long tail

Reference 46

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This paper cites What makes a good dataset for knowledge distillation? arXiv preprint arXiv:2411.12817,.

Active Data Curation Effectively Distills Large-Scale Multimodal Models What makes a good dataset for knowledge distillation? arXiv preprint arXiv:2411.12817,

Reference 47

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Dat- acomp: In search of the next generation of multimodal datasets

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Clip-adapter: Better vision-language models with feature adapters

Reference 49

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Training Task Experts through Retrieval Based Distillation

Reference 50

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Are we ready for autonomous driving? the kitti vision benchmark suite

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Knowledge distillation: A survey

Reference 52

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Scaling laws for data filtering–data curation cannot be compute agnostic

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Bootstrap your own latent-a new approach to self-supervised learning

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Self-Knowledge Distillation in Natural Language Processing

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Active Data Curation Effectively Distills Large-Scale Multimodal Models AMD: Automatic Multi-step Distillation of Large-scale Vision Models

Reference 56

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Observation f8c8e86a-b0e2-4e48-af0b-86f4edb67226 · outbound

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Revisit the power of vanilla knowledge distillation: from small scale to large scale

Reference 57

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Statis- tical meta-analysis with applications

Reference 58

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Knowledge distillation as efficient pre-training: Faster convergence, higher data-efficiency, and better trans- ferability

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification, 2017

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Active Data Curation Effectively Distills Large-Scale Multimodal Models The many faces of robust- ness: A critical analysis of out-of-distribution generalization

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Natural adversarial examples

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Knowledge distillation with adversarial samples sup- porting decision boundary

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Active Data Curation Effectively Distills Large-Scale Multimodal Models NWPU-RESISC45 Dataset with 12 classes

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Distilling the Knowledge in a Neural Network

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Diversified Batch Selection for Training Acceleration

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Online batch selection for enhanced generalization in imbalanced datasets

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Show, attend and distill: Knowledge distillation via attention-based fea- ture matching

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Scaling up visual and vision-language representa- tion learning with noisy text supervision

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Accelerating Deep Learning by Focusing on the Biggest Losers

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Active Data Curation Effectively Distills Large-Scale Multimodal Models TinyBERT: Distilling BERT for Natural Language Understanding

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Clevr: A diagnostic dataset for compositional language and ele- mentary visual reasoning

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Submodular Batch Selection for Training Deep Neural Networks

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Not all sam- ples are created equal: Deep learning with importance sam- pling

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Active Data Curation Effectively Distills Large-Scale Multimodal Models HYPE: Hyperbolic Entailment Filtering for Underspecified Images and Texts

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Sequence-Level Knowledge Distillation

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Big transfer (bit): General visual representation learning

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Active Data Curation Effectively Distills Large-Scale Multimodal Models 3d object representations for fine-grained categorization

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Learning multiple layers of features from tiny images

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Active Data Curation Effectively Distills Large-Scale Multimodal Models SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Self-paced learning for latent variable models

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Active Data Curation Effectively Distills Large-Scale Multimodal Models VeCLIP: Improving CLIP Training via Visual-enriched Captions

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Improve Knowledge Distillation via Label Revision and Data Selection

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Modeling Caption Diversity in Contrastive Vision-Language Pretraining

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Set transformer: A framework for attention-based permutation-invariant neural networks

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Caltech 101, 2022

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Blip- 2: Bootstrapping language-image pre-training with frozen image encoders and large language models

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Active Data Curation Effectively Distills Large-Scale Multimodal Models DataComp-LM: In search of the next generation of training sets for language models

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Dynamic Knowledge Distillation for Pre-trained Language Models

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Active Data Curation Effectively Distills Large-Scale Multimodal Models What If We Recaption Billions of Web Images with LLaMA-3?

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Promptkd: Unsupervised prompt distillation for vision-language models

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Active Data Curation Effectively Distills Large-Scale Multimodal Models Efficient Sub-structured Knowledge Distillation

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