Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection | Paper Citation Record

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This paper cites Contextual diversity for active learning.

Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection Contextual diversity for active learning

Reference 1

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

Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection Food-101--mining discriminative components with random forests

Reference 2

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This paper cites Emerging properties in self-supervised vision transformers.

Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection Emerging properties in self-supervised vision transformers

Reference 3

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This paper cites Selection via Proxy: Efficient Data Selection for Deep Learning.

Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection Selection via Proxy: Efficient Data Selection for Deep Learning

Reference 4

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This paper cites Class-balanced loss based on effective number of samples.

Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection Class-balanced loss based on effective number of samples

Reference 5

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

Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection Imagenet: A large-scale hierarchical image database

Reference 6

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This paper cites Parameter-efficient fine-tuning of large-scale pre-trained language models.

Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection Parameter-efficient fine-tuning of large-scale pre-trained language models

Reference 7

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This paper cites Adversarial Active Learning for Deep Networks: a Margin Based Approach.

Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection Adversarial Active Learning for Deep Networks: a Margin Based Approach

Reference 8

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Observation c3a6dabc-ff8d-4776-b740-b05d0c980d06 · outbound

This paper cites Clipcleaner: Cleaning noisy labels with clip.

Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection Clipcleaner: Cleaning noisy labels with clip

Reference 9

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Observation 8a2ee412-268d-44fb-b9c2-19844d4daebe · outbound

This paper cites DeepCore: A Comprehensive Library for Coreset Selection in Deep Learning.

Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection DeepCore: A Comprehensive Library for Coreset Selection in Deep Learning

Reference 10

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Observation cb743829-1c12-4c2c-af49-1ca2d8f4bf90 · outbound

This paper cites Deep residual learning for image recognition.

Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection Deep residual learning for image recognition

Reference 11

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Observation 32e57e1e-45b0-4225-97c7-bc6809eca26d · outbound

This paper cites Large-scale dataset pruning with dynamic uncertainty.

Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection Large-scale dataset pruning with dynamic uncertainty

Reference 12

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This paper cites Submodular combinatorial information measures with applications in machine learning.

Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection Submodular combinatorial information measures with applications in machine learning

Reference 13

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This paper cites Balancing privacy and performance: A many-in-one approach for image anonymization.

Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection Balancing privacy and performance: A many-in-one approach for image anonymization

Reference 14

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This paper cites Orient: Submodular mutual information measures for data subset selection under distribution shift.

Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection Orient: Submodular mutual information measures for data subset selection under distribution shift

Reference 15

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This paper cites Grad-match: Gradient matching based data subset selection for efficient deep model training.

Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection Grad-match: Gradient matching based data subset selection for efficient deep model training

Reference 16

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Observation 4bdb4041-0f9d-4a24-949e-ade4963dbfc5 · outbound

This paper cites Glister: Generalization based data subset selection for efficient and robust learning.

Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection Glister: Generalization based data subset selection for efficient and robust learning

Reference 17

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This paper cites S., Lnu, A., Ramakrishnan, G., Evfimievski, A., Popa, L., and Iyer, R.

Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection S., Lnu, A., Ramakrishnan, G., Evfimievski, A., Popa, L., and Iyer, R

Reference 18

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Observation 71f34745-f77c-44e6-a476-78f30aaa04a7 · outbound

This paper cites MILO: Model-Agnostic Subset Selection Framework for Efficient Model Training and Tuning.

Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection MILO: Model-Agnostic Subset Selection Framework for Efficient Model Training and Tuning

Reference 19

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This paper cites Prism: A rich class of parameterized submodular information measures for guided data subset selection.

Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection Prism: A rich class of parameterized submodular information measures for guided data subset selection

Reference 20

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

Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection Learning multiple layers of features from tiny images

Reference 21

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Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection Unresolved cited work

Reference 22

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Observation b461d20a-0f00-49b9-b00d-b4aa59de2d0c · outbound

This paper cites Active Learning by Acquiring Contrastive Examples.

Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection Active Learning by Acquiring Contrastive Examples

Reference 23

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This paper cites Coresets for data-efficient training of machine learning models.

Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection Coresets for data-efficient training of machine learning models

Reference 24

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This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection DINOv2: Learning Robust Visual Features without Supervision

Reference 25

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This paper cites M., Vedaldi, A., Zisserman, A., and Jawahar, C.

Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection M., Vedaldi, A., Zisserman, A., and Jawahar, C

Reference 26

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Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection Unresolved cited work

Reference 27

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Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection Unresolved cited work

Reference 28

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This paper cites W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.

Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al

Reference 29

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This paper cites Active Learning for Convolutional Neural Networks: A Core-Set Approach.

Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection Active Learning for Convolutional Neural Networks: A Core-Set Approach

Reference 30

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This paper cites EVA-CLIP: Improved Training Techniques for CLIP at Scale.

Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection EVA-CLIP: Improved Training Techniques for CLIP at Scale

Reference 31

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This paper cites Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics.

Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics

Reference 32

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Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection Unresolved cited work

Reference 33

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

Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection The caltech-ucsd birds-200-2011 dataset

Reference 34

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This paper cites A survey of dataset refinement for problems in computer vision datasets.

Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection A survey of dataset refinement for problems in computer vision datasets

Reference 35

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This paper cites Contributing dimension structure of deep feature for coreset selection.

Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection Contributing dimension structure of deep feature for coreset selection

Reference 36

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This paper cites The parables of the mustard seed and the yeast: Extremely low-budget, high-performance nighttime semantic segmentation.

Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection The parables of the mustard seed and the yeast: Extremely low-budget, high-performance nighttime semantic segmentation

Reference 37

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This paper cites Learning with noisy labels revisited: A study using real-world human annotations.

Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection Learning with noisy labels revisited: A study using real-world human annotations

Reference 38

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Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection Herding dynamical weights to learn

Reference 39

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This paper cites HuggingFace's Transformers: State-of-the-art Natural Language Processing.

Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection HuggingFace's Transformers: State-of-the-art Natural Language Processing

Reference 40

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This paper cites Assess and guide: Multi-modal fake news detection via decision uncertainty.

Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection Assess and guide: Multi-modal fake news detection via decision uncertainty

Reference 41

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This paper cites LESS : Selecting influential data for targeted instruction tuning.

Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection LESS : Selecting influential data for targeted instruction tuning

Reference 42

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This paper cites Moderate coreset: A universal method of data selection for real-world data-efficient deep learning.

Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection Moderate coreset: A universal method of data selection for real-world data-efficient deep learning

Reference 43

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This paper cites Towards free data selection with general-purpose models.

Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection Towards free data selection with general-purpose models

Reference 44

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This paper cites Mind the boundary: Coreset selection via reconstructing the decision boundary.

Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection Mind the boundary: Coreset selection via reconstructing the decision boundary

Reference 45

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This paper cites Sigmoid loss for language image pre-training, 2023.

Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection Sigmoid loss for language image pre-training, 2023

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:23:31.247565Z

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Observation 3e527f7c-c549-4efb-a6a6-4a1f7408e9c8 · outbound

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Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection Unresolved cited work

Reference 47

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Observation 3990c533-352a-4c60-a394-81ed3c9993ea · outbound

This paper cites Coverage-centric Coreset Selection for High Pruning Rates.

Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection Coverage-centric Coreset Selection for High Pruning Rates

Reference 48

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This paper cites Coverage-centric coreset selection for high pruning rates.

Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection Coverage-centric coreset selection for high pruning rates

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:23:30.776116Z

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Observation 0069541b-3507-4bcb-942f-dc57a5f5bb35 · outbound

This paper cites Curriculum learning by dynamic instance hardness.

Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection Curriculum learning by dynamic instance hardness

Reference 50

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