Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels | Paper Citation Record

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This paper cites Rainbow memory: Continual learn- ing with a memory of diverse samples.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels Rainbow memory: Continual learn- ing with a memory of diverse samples

Reference 1

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Observation 907415f9-b4c5-4bed-9d1b-5d2137735f12 · outbound

This paper cites Scail: Classifier weights scaling for class incremental learning.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels Scail: Classifier weights scaling for class incremental learning

Reference 2

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Observation c07f7126-71bd-42d9-9b80-ae326b3bdd5c · outbound

This paper cites Deep clustering for unsupervised learning of visual features.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels Deep clustering for unsupervised learning of visual features

Reference 3

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Observation 78f5de38-0819-4f1d-ab92-cca480e38e46 · outbound

This paper cites Riemannian walk for incremen- tal learning: Understanding forgetting and intransigence.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels Riemannian walk for incremen- tal learning: Understanding forgetting and intransigence

Reference 4

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Observation 433386b1-18b4-4f1a-a2e1-bd5ebfd5f4a2 · outbound

This paper cites Contrastive mean- shift learning for generalized category discovery.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels Contrastive mean- shift learning for generalized category discovery

Reference 5

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Observation df28ceb8-0e03-45bb-8324-c80f2b9e1ac2 · outbound

This paper cites AutoAugment: Learning Augmentation Policies from Data.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels AutoAugment: Learning Augmentation Policies from Data

Reference 6

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Observation e3d3da27-112f-40b3-a8e8-317e33baf39d · outbound

This paper cites A survey on network embedding.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels A survey on network embedding

Reference 7

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Observation b387d25f-94e1-4c25-8cb8-fdf8e8bb50fe · outbound

This paper cites Dytox: Transformers for continual learning with dynamic token expansion.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels Dytox: Transformers for continual learning with dynamic token expansion

Reference 8

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Observation 3311539c-3061-423c-8bf4-ecf173bfa129 · outbound

This paper cites XCon: Learning with Experts for Fine-grained Category Discovery.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels XCon: Learning with Experts for Fine-grained Category Discovery

Reference 9

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Observation a949e4fd-f4b5-419c-8e81-3f83a3ab61e8 · outbound

This paper cites Quick-means: accelerating inference for k-means by learning fast transforms.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels Quick-means: accelerating inference for k-means by learning fast transforms

Reference 10

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Observation e1176f55-b0fb-4e47-a137-e2af01995ab6 · outbound

This paper cites Automatically Discovering and Learning New Visual Categories with Ranking Statistics.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels Automatically Discovering and Learning New Visual Categories with Ranking Statistics

Reference 11

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Observation 4470abb5-522c-4543-86fa-6ef0fa91fed8 · outbound

This paper cites Autonovel: Automati- cally discovering and learning novel visual categories.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels Autonovel: Automati- cally discovering and learning novel visual categories

Reference 12

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Observation 56b4bfe4-2ca2-4441-b664-7c3fdc88222a · outbound

This paper cites Unsupervised contin- ual learning via pseudo labels.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels Unsupervised contin- ual learning via pseudo labels

Reference 13

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Observation 3d5150b3-1ebc-4b81-8e4f-e40c5c43222c · outbound

This paper cites Deep residual learning for image recognition.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels Deep residual learning for image recognition

Reference 14

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Observation 58e6cd80-9fd8-4350-967f-7632f03f7bf2 · outbound

This paper cites Rethinking im- agenet pre-training.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels Rethinking im- agenet pre-training

Reference 15

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Observation dad8a971-7d72-4ce3-816c-d798de469ec6 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels Distilling the Knowledge in a Neural Network

Reference 16

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Observation 903df8e0-df11-424b-9cc7-660db14d2623 · outbound

This paper cites Learning a unified classifier incrementally via rebalancing.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels Learning a unified classifier incrementally via rebalancing

Reference 17

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Observation 0da065f0-54a3-402d-bafa-466c7dae8eee · outbound

This paper cites Calibrated neighborhood aware confidence measure for deep metric learning.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels Calibrated neighborhood aware confidence measure for deep metric learning

Reference 18

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Observation 7fd20ffe-fcf2-4972-93ea-3c8c4cf5caf4 · outbound

This paper cites Unsupervised Class-Incremental Learning Through Confusion.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels Unsupervised Class-Incremental Learning Through Confusion

Reference 19

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Observation 8db47a35-6580-4033-a346-fb501c26be36 · outbound

This paper cites Proxy anchor-based unsu- pervised learning for continuous generalized category dis- covery.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels Proxy anchor-based unsu- pervised learning for continuous generalized category dis- covery

Reference 20

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Observation c3644d85-7fe4-4636-a2a3-b35fd00a8b4e · outbound

This paper cites 3d object representations for fine-grained categorization.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels 3d object representations for fine-grained categorization

Reference 21

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Observation 3f29e040-9e4d-41ee-8455-aef77683f13c · outbound

This paper cites Learning multiple layers of features from tiny images.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels Learning multiple layers of features from tiny images

Reference 22

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Observation d20e0fb9-0533-49c4-ab59-03a55d5d0d92 · outbound

This paper cites The hungarian method for the assignment problem.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels The hungarian method for the assignment problem

Reference 23

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Observation 478659cd-9bb0-4d35-8126-e494723bc403 · outbound

This paper cites Pseudo-label: The simple and effi- cient semi-supervised learning method for deep neural net- works.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels Pseudo-label: The simple and effi- cient semi-supervised learning method for deep neural net- works

Reference 24

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Observation 5e74d790-4101-4b39-9213-a99a26df96da · outbound

This paper cites Large scale k- means clustering using gpus.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels Large scale k- means clustering using gpus

Reference 25

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Observation e878bed3-2b4d-4ea9-b102-fb37ebaaa957 · outbound

This paper cites Learning without forgetting.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels Learning without forgetting

Reference 26

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Observation f7e88c75-72aa-4dd8-be44-f140260a5bda · outbound

This paper cites Least squares quantization in pcm.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels Least squares quantization in pcm

Reference 27

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Observation 20891243-4797-4fd7-9de5-c242cb926f9f · outbound

This paper cites an unresolved cited work.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels Unresolved cited work

Reference 28

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Observation 35edd8b4-c37b-4376-9151-b76f51d13c15 · outbound

This paper cites Catastrophic inter- ference in connectionist networks: The sequential learning problem.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels Catastrophic inter- ference in connectionist networks: The sequential learning problem

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:26:02.632554Z

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Observation 33096234-0157-40fb-abd4-ff5ccaeb8063 · outbound

This paper cites Modern hierarchical, agglomerative clustering algorithms.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels Modern hierarchical, agglomerative clustering algorithms

Reference 30

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Observation 239823b1-b2d9-4089-a1b2-2a3d1c14f01b · outbound

This paper cites Cats and dogs.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels Cats and dogs

Reference 31

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Observation 12c44a2a-1f46-4707-80ba-fbaa73bb2a5c · outbound

This paper cites Pytorch: An im- perative style, high-performance deep learning library.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels Pytorch: An im- perative style, high-performance deep learning library

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:26:02.576615Z

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Observation 0852e06c-e79f-4695-8f7e-9f2e80698cbb · outbound

This paper cites Scikit-learn: Machine learning in python.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels Scikit-learn: Machine learning in python

Reference 33

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This paper cites Dynamic conceptional contrastive learning for generalized category discovery.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels Dynamic conceptional contrastive learning for generalized category discovery

Reference 34

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Observation 2bc3f267-f275-4e82-b042-40c5d48141a3 · outbound

This paper cites icarl: Incremental classifier and representation learning.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels icarl: Incremental classifier and representation learning

Reference 35

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Observation 6fe2421d-cf7b-4ec2-acee-455570c3fe08 · outbound

This paper cites Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference

Reference 36

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This paper cites Semi-supervised self-training of object detection mod- els.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels Semi-supervised self-training of object detection mod- els

Reference 37

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Observation e87a5f3d-ce94-488b-9cbb-411b6bca51e2 · outbound

This paper cites Class-incremental novel class discovery.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels Class-incremental novel class discovery

Reference 38

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This paper cites Imagenet large scale visual recognition challenge.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels Imagenet large scale visual recognition challenge

Reference 39

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This paper cites When to Accept Automated Predictions and When to Defer to Human Judgment?.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels When to Accept Automated Predictions and When to Defer to Human Judgment?

Reference 40

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This paper cites Generalized category discovery.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels Generalized category discovery

Reference 41

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Observation ddac91a6-9a27-4d51-a0c2-70f637ebbde5 · outbound

This paper cites A tutorial on spectral clustering.Statis- tics and computing, 17:395–416, 2007.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels A tutorial on spectral clustering.Statis- tics and computing, 17:395–416, 2007

Reference 42

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Observation 5d5144eb-b21f-4d1a-b072-03692de14b31 · outbound

This paper cites Foster: Feature boosting and compression for class- incremental learning.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels Foster: Feature boosting and compression for class- incremental learning

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:26:02.361686Z

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Observation f64cb4b4-d3f0-465b-a676-cb6d38003f07 · outbound

This paper cites Herding dynamical weights to learn.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels Herding dynamical weights to learn

Reference 44

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This paper cites Ft k-means: A high-performance k-means on gpu with fault tolerance.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels Ft k-means: A high-performance k-means on gpu with fault tolerance

Reference 45

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Observation 4206b4e3-2de5-4f9e-bc9b-11b41478053f · outbound

This paper cites Large scale incre- mental learning.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels Large scale incre- mental learning

Reference 46

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This paper cites Survey of clustering algo- rithms.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels Survey of clustering algo- rithms

Reference 47

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Observation cd8b190d-1e7a-40e3-99ff-f3c87b60e39f · outbound

This paper cites Class-weighted classification: Trade-offs and robust approaches.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels Class-weighted classification: Trade-offs and robust approaches

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:26:02.237827Z

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Observation 60fa458b-1fdf-4a92-9df8-9a512bdc61a9 · outbound

This paper cites Der: Dy- namically expandable representation for class incremental learning.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels Der: Dy- namically expandable representation for class incremental learning

Reference 49

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This paper cites Online deep clustering for unsupervised representation learning.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels Online deep clustering for unsupervised representation learning

Reference 50

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Observation e721bf68-0547-44f7-85ac-ed4cc3e00ae3 · outbound

This paper cites Flexmatch: Boosting semi-supervised learning with curricu- lum pseudo labeling.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels Flexmatch: Boosting semi-supervised learning with curricu- lum pseudo labeling

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:26:01.307600Z

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Observation 9d8ac6f3-90ad-4193-91a3-4e267acfca2b · outbound

This paper cites mixup: Beyond Empirical Risk Minimization.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels mixup: Beyond Empirical Risk Minimization

Reference 52

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This paper cites Grow and merge: A unified framework for continu- ous categories discovery.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels Grow and merge: A unified framework for continu- ous categories discovery

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:26:01.104211Z

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Observation 054a9ea2-4f90-414d-8519-308f7e7157eb · outbound

This paper cites Novel visual category discov- ery with dual ranking statistics and mutual knowledge distil- lation.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels Novel visual category discov- ery with dual ranking statistics and mutual knowledge distil- lation

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:26:00.897495Z

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Observation 25a0602b-3532-4266-b5ef-dbfba16a83da · outbound

This paper cites Maintaining discrimination and fairness in class incremental learning.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels Maintaining discrimination and fairness in class incremental learning

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:26:00.699775Z

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Observation e00a20e0-0cf6-4c89-885c-946893093764 · outbound

This paper cites Class-Incremental Learning: A Survey.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels Class-Incremental Learning: A Survey

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-05T14:25:59.271945Z

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Unavailable: canonical work link unavailable.

Observation fb4bcb91-3d35-4f34-a3c9-cf5327e2c765 · outbound

This paper cites Class-incremental learning via dual augmentation.

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels Class-incremental learning via dual augmentation

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:26:00.493143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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