SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency | Paper Citation Record

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This paper cites Lawrence Zitnick, and Devi Parikh.

SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Lawrence Zitnick, and Devi Parikh

Reference 2

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This paper cites Beit: BERT pre-training of image transformers.

SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Beit: BERT pre-training of image transformers

Reference 3

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This paper cites Clip retrieval: Easily compute clip em- beddings and build a clip retrieval system with them, 2022.

SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Clip retrieval: Easily compute clip em- beddings and build a clip retrieval system with them, 2022

Reference 4

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This paper cites Bowyer and Patrick J.

SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Bowyer and Patrick J

Reference 5

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This paper cites Emerg- ing properties in self-supervised vision transformers.

SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Emerg- ing properties in self-supervised vision transformers

Reference 6

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

SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts

Reference 7

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SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Unresolved cited work

Reference 8

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Reference 9

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Observation 1c0d14cf-e9f4-4d0d-9102-b3acbddcf19a · outbound

This paper cites Girshick, and Kaiming He.

SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Girshick, and Kaiming He

Reference 10

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This paper cites An empirical study of training self-supervised vision transformers.

SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency An empirical study of training self-supervised vision transformers

Reference 11

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This paper cites Data distillation can be like vodka: Distilling more times for better quality.

SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Data distillation can be like vodka: Distilling more times for better quality

Reference 12

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This paper cites Imagenet: A large-scale hierarchical image database.

SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Imagenet: A large-scale hierarchical image database

Reference 13

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

SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency An image is worth 16x16 words: Transformers for image recognition at scale

Reference 14

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This paper cites Sequential subset matching for dataset distillation.

SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Sequential subset matching for dataset distillation

Reference 15

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This paper cites Second thoughts on the bootstrap.

SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Second thoughts on the bootstrap

Reference 16

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This paper cites Rigging the lottery: Making all tickets winners.

SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Rigging the lottery: Making all tickets winners

Reference 17

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This paper cites What neural networks memorize and why: Discovering the long tail via influence estimation.

SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency What neural networks memorize and why: Discovering the long tail via influence estimation

Reference 18

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This paper cites Simcse: Sim- ple contrastive learning of sentence embeddings.

SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Simcse: Sim- ple contrastive learning of sentence embeddings

Reference 19

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This paper cites A closer look at deep learning heuristics: Learning rate restarts, warmup and distillation.

SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency A closer look at deep learning heuristics: Learning rate restarts, warmup and distillation

Reference 20

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This paper cites Deep residual learning for image recognition.

SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Deep residual learning for image recognition

Reference 21

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This paper cites Girshick.

SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Girshick

Reference 22

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This paper cites The many faces of robustness: A critical analysis of out-of-distribution generalization.

SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency The many faces of robustness: A critical analysis of out-of-distribution generalization

Reference 23

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This paper cites Clipscore: A reference-free evaluation metric for image captioning.

SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Clipscore: A reference-free evaluation metric for image captioning

Reference 24

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This paper cites Rae, and Laurent Sifre.

SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Rae, and Laurent Sifre

Reference 25

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This paper cites Openclip, 2021.

SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Openclip, 2021

Reference 26

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This paper cites Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig.

SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig

Reference 27

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This paper cites Scaling Laws for Neural Language Models.

SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Scaling Laws for Neural Language Models

Reference 28

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This paper cites Understanding black-box predictions via influence functions.

SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Understanding black-box predictions via influence functions

Reference 29

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

SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Learning multiple layers of features from tiny images

Reference 30

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This paper cites Tiny imagenet visual recognition challenge.

SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Tiny imagenet visual recognition challenge

Reference 31

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SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Unresolved cited work

Reference 32

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This paper cites Error norm truncation: Robust training in the presence of data noise for text generation models.

SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Error norm truncation: Robust training in the presence of data noise for text generation models

Reference 33

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This paper cites UP-DP: unsupervised prompt learning for data pre-selection with vision-language models.

SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency UP-DP: unsupervised prompt learning for data pre-selection with vision-language models

Reference 34

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This paper cites An inverse scaling law for CLIP training.

SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency An inverse scaling law for CLIP training

Reference 35

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This paper cites Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C.

SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C

Reference 36

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This paper cites Do we actually need dense over- parameterization? in-time over-parameterization in sparse training.

SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Do we actually need dense over- parameterization? in-time over-parameterization in sparse training

Reference 37

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This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Swin transformer: Hierarchical vision transformer using shifted windows

Reference 38

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SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency SGDR: stochastic gradient descent with warm restarts

Reference 39

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SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency D2 pruning: Message passing for balancing diversity and diffi- culty in data pruning

Reference 40

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This paper cites SIEVE: multimodal dataset pruning using image captioning models.

SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency SIEVE: multimodal dataset pruning using image captioning models

Reference 41

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This paper cites Bilmes, and Jure Leskovec.

SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Bilmes, and Jure Leskovec

Reference 42

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This paper cites Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science.

SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science

Reference 43

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SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Using relevance to reduce network size automatically

Reference 44

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This paper cites Fantastic weights and how to find them: Where to prune in dynamic sparse training.

SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Fantastic weights and how to find them: Where to prune in dynamic sparse training

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SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Unresolved cited work

Reference 46

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This paper cites Deep learning on a data diet: Finding important examples early in training.

SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Deep learning on a data diet: Finding important examples early in training

Reference 47

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This paper cites Infobatch: Loss- less training speed up by unbiased dynamic data pruning.

SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Infobatch: Loss- less training speed up by unbiased dynamic data pruning

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

SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Learning transferable visual models from natural language supervision

Reference 49

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This paper cites Do imagenet classifiers generalize to ima- genet? In ICML, pages 5389–5400.

SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Do imagenet classifiers generalize to ima- genet? In ICML, pages 5389–5400

Reference 50

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This paper cites Laion-400m: Open dataset of clip-filtered 400 million image-text pairs.

SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Laion-400m: Open dataset of clip-filtered 400 million image-text pairs

Reference 51

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Observation c1133512-1900-4d72-9607-0542241dab03 · outbound

This paper cites LAION- 5B: an open large-scale dataset for training next generation image-text models.

SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency LAION- 5B: an open large-scale dataset for training next generation image-text models

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This paper cites MIM4DD: mutual information maximization for dataset distillation.

SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency MIM4DD: mutual information maximization for dataset distillation

Reference 53

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SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning

Reference 54

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Reference 55

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SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency An introduction to the bootstrap (bradley efron and robert j

Reference 56

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SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Beyond neural scaling laws: beating power law scaling via data pruning

Reference 57

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This paper cites On the diversity and realism of distilled dataset: An efficient dataset distilla- tion paradigm.

SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency On the diversity and realism of distilled dataset: An efficient dataset distilla- tion paradigm

Reference 58

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SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Unresolved cited work

Reference 59

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SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Training data-efficient image transformers & distillation through atten- tion

Reference 60

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SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Represen- tation learning with contrastive predictive coding

Reference 61

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SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Gomez, Lukasz Kaiser, and Illia Polosukhin

Reference 62

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SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Too large; data reduction for vision-language pre-training

Reference 63

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SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Finetuned Multimodal Language Models Are High-Quality Image-Text Data Filters

Reference 64

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SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency CLIPLoss and Norm-Based Data Selection Methods for Multimodal Contrastive Learning

Reference 65

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Observation 35b055a3-a1fa-47a8-b713-b4a4bd57d8b2 · outbound

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SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency On the de-duplication of LAION-2B

Reference 66

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This paper cites Cit: Curation in training for effective vision- language data.

SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Cit: Curation in training for effective vision- language data

Reference 67

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SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Demystifying CLIP data

Reference 68

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SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Dataset pruning: Reducing training data by examining generalization influence

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:07:00.533426Z

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Observation 684baba6-4666-43e2-a2c0-b08017acf6ec · outbound

This paper cites Coca: Contrastive captioners are image-text foundation models.

SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Coca: Contrastive captioners are image-text foundation models

Reference 70

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Observation 1a0b8556-6755-446b-af5a-26ad652819cb · outbound

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SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency MEST: accurate and fast memory- economic sparse training framework on the edge

Reference 71

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SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Dynamic sparse no training: Training-free fine-tuning for sparse llms

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:07:00.456687Z

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This paper cites Learning to prompt for vision-language models.

SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency Learning to prompt for vision-language models

Reference 73

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