T2UE: Generating Unlearnable Examples from Text Descriptions | Paper Citation Record

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This paper cites Large image datasets: A pyrrhic win for computer vision? In WACV, 2021.

T2UE: Generating Unlearnable Examples from Text Descriptions Large image datasets: A pyrrhic win for computer vision? In WACV, 2021

Reference 1

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Observation 2ed96b91-ddab-4159-9191-dda38d3ba10b · outbound

This paper cites Microsoft COCO Captions: Data Collection and Evaluation Server.

T2UE: Generating Unlearnable Examples from Text Descriptions Microsoft COCO Captions: Data Collection and Evaluation Server

Reference 2

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

T2UE: Generating Unlearnable Examples from Text Descriptions An analysis of single-layer networks in unsuper- vised feature learning

Reference 3

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This paper cites Cubuk, Barret Zoph, Dandelion Mané, Vijay Vasudevan, and Quoc V.

T2UE: Generating Unlearnable Examples from Text Descriptions Cubuk, Barret Zoph, Dandelion Mané, Vijay Vasudevan, and Quoc V

Reference 4

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This paper cites Improved Regularization of Convolutional Neural Networks with Cutout.

T2UE: Generating Unlearnable Examples from Text Descriptions Improved Regularization of Convolutional Neural Networks with Cutout

Reference 5

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

T2UE: Generating Unlearnable Examples from Text Descriptions An image is worth 16x16 words: Transformers for image recognition at scale

Reference 6

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This paper cites Adversarial examples make strong poisons.

T2UE: Generating Unlearnable Examples from Text Descriptions Adversarial examples make strong poisons

Reference 7

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This paper cites Robust unlearnable exam- ples: Protecting data against adversarial learning.

T2UE: Generating Unlearnable Examples from Text Descriptions Robust unlearnable exam- ples: Protecting data against adversarial learning

Reference 8

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This paper cites Datacomp: In search of the next generation of multimodal datasets.

T2UE: Generating Unlearnable Examples from Text Descriptions Datacomp: In search of the next generation of multimodal datasets

Reference 9

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Observation a2f30960-d311-457e-97e2-8dcd1286bc8a · outbound

This paper cites ARMOR: Shielding Unlearnable Examples against Data Augmentation.

T2UE: Generating Unlearnable Examples from Text Descriptions ARMOR: Shielding Unlearnable Examples against Data Augmentation

Reference 10

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Observation 8b6f28c6-d195-4ad0-af41-b9e8be93d262 · outbound

This paper cites Medgan: An adaptive gan approach for medical image generation.

T2UE: Generating Unlearnable Examples from Text Descriptions Medgan: An adaptive gan approach for medical image generation

Reference 11

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Observation 9ed92911-faf5-4fae-82e6-ac6e8a08d454 · outbound

This paper cites Indiscriminate poisoning attacks on unsupervised contrastive learning.

T2UE: Generating Unlearnable Examples from Text Descriptions Indiscriminate poisoning attacks on unsupervised contrastive learning

Reference 12

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Observation 8426dc71-04c7-43cb-87f8-491c0668138d · outbound

This paper cites Deep residual learning for image recognition.

T2UE: Generating Unlearnable Examples from Text Descriptions Deep residual learning for image recognition

Reference 13

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This paper cites The secretive company that might end privacy as we know it.

T2UE: Generating Unlearnable Examples from Text Descriptions The secretive company that might end privacy as we know it

Reference 14

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This paper cites Densely con- nected convolutional networks.

T2UE: Generating Unlearnable Examples from Text Descriptions Densely con- nected convolutional networks

Reference 15

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Observation a72eb959-2e62-4bc7-9b3b-b2679a9e5f09 · outbound

This paper cites Un- learnable examples: Making personal data unexploitable.

T2UE: Generating Unlearnable Examples from Text Descriptions Un- learnable examples: Making personal data unexploitable

Reference 16

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This paper cites Unlearnable examples: Protecting open-source software from unauthorized neural code learning.

T2UE: Generating Unlearnable Examples from Text Descriptions Unlearnable examples: Protecting open-source software from unauthorized neural code learning

Reference 17

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Observation 2ea659dc-30ee-4ae6-8bbd-7b5aca0edc28 · outbound

This paper cites Scaling up visual and vision-language representation learning with noisy text supervision.

T2UE: Generating Unlearnable Examples from Text Descriptions Scaling up visual and vision-language representation learning with noisy text supervision

Reference 18

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This paper cites Unlearnable examples for time series.

T2UE: Generating Unlearnable Examples from Text Descriptions Unlearnable examples for time series

Reference 19

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Observation 67a31f4c-5517-4347-80af-43c9a116a828 · outbound

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

T2UE: Generating Unlearnable Examples from Text Descriptions Understanding black-box predictions via influence functions

Reference 20

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Observation b0b4358c-62cd-4aa1-8781-9d0c26891c76 · outbound

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

T2UE: Generating Unlearnable Examples from Text Descriptions Learning multiple layers of features from tiny images

Reference 21

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Observation 9db3608b-6cdc-429b-97a0-3eaa4877dc5a · outbound

This paper cites Text to Image Generation with Semantic-Spatial Aware GAN.

T2UE: Generating Unlearnable Examples from Text Descriptions Text to Image Generation with Semantic-Spatial Aware GAN

Reference 22

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This paper cites Text to image generation with semantic-spatial aware gan.

T2UE: Generating Unlearnable Examples from Text Descriptions Text to image generation with semantic-spatial aware gan

Reference 23

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This paper cites Microsoft coco: Common objects in context.

T2UE: Generating Unlearnable Examples from Text Descriptions Microsoft coco: Common objects in context

Reference 24

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This paper cites Multimodal unlearnable examples: Protecting data against multimodal contrastive learning.

T2UE: Generating Unlearnable Examples from Text Descriptions Multimodal unlearnable examples: Protecting data against multimodal contrastive learning

Reference 25

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This paper cites Stable unlearnable example: Enhancing the robustness of unlearnable examples via stable error-minimizing noise.

T2UE: Generating Unlearnable Examples from Text Descriptions Stable unlearnable example: Enhancing the robustness of unlearnable examples via stable error-minimizing noise

Reference 26

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This paper cites The data provenance initiative: A large scale audit of dataset licensing & attribution in ai.

T2UE: Generating Unlearnable Examples from Text Descriptions The data provenance initiative: A large scale audit of dataset licensing & attribution in ai

Reference 27

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Observation e21bbb09-7a71-45d0-9066-d4a3bf2925c7 · outbound

This paper cites Safety at Scale: A Comprehensive Survey of Large Model and Agent Safety.

T2UE: Generating Unlearnable Examples from Text Descriptions Safety at Scale: A Comprehensive Survey of Large Model and Agent Safety

Reference 28

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This paper cites Conditional generative adversarial nets.

T2UE: Generating Unlearnable Examples from Text Descriptions Conditional generative adversarial nets

Reference 29

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Observation ef11bb78-6e60-46e3-85a6-c1c520b88859 · outbound

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

T2UE: Generating Unlearnable Examples from Text Descriptions Learning transferable visual models from natural language supervision

Reference 30

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Observation fdf6a9d2-ddb1-46ff-ba94-faf2d2e858cb · outbound

This paper cites Transferable Unlearnable Examples.

T2UE: Generating Unlearnable Examples from Text Descriptions Transferable Unlearnable Examples

Reference 31

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This paper cites Cuda: Convolution-based unlearnable datasets.

T2UE: Generating Unlearnable Examples from Text Descriptions Cuda: Convolution-based unlearnable datasets

Reference 32

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Observation 9ace8b4c-eb5f-4b12-9b93-c21c572c086d · outbound

This paper cites Autoregressive perturbations for data poisoning.

T2UE: Generating Unlearnable Examples from Text Descriptions Autoregressive perturbations for data poisoning

Reference 33

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This paper cites What can we learn from unlearnable datasets? NeurIPS, 2023.

T2UE: Generating Unlearnable Examples from Text Descriptions What can we learn from unlearnable datasets? NeurIPS, 2023

Reference 34

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This paper cites LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs.

T2UE: Generating Unlearnable Examples from Text Descriptions LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs

Reference 35

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This paper cites Laion- 5b: An open large-scale dataset for training next generation image-text models.NeurIPS, 2022.

T2UE: Generating Unlearnable Examples from Text Descriptions Laion- 5b: An open large-scale dataset for training next generation image-text models.NeurIPS, 2022

Reference 36

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This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

T2UE: Generating Unlearnable Examples from Text Descriptions Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 37

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This paper cites Unseg: One universal unlearnable example generator is enough against all image segmentation.

T2UE: Generating Unlearnable Examples from Text Descriptions Unseg: One universal unlearnable example generator is enough against all image segmentation

Reference 38

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This paper cites Going deeper with convolutions.

T2UE: Generating Unlearnable Examples from Text Descriptions Going deeper with convolutions

Reference 39

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This paper cites Generating videos with scene dy- namics.

T2UE: Generating Unlearnable Examples from Text Descriptions Generating videos with scene dy- namics

Reference 40

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This paper cites Provably Unlearnable Data Examples.

T2UE: Generating Unlearnable Examples from Text Descriptions Provably Unlearnable Data Examples

Reference 41

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This paper cites GIT: A Generative Image-to-text Transformer for Vision and Language.

T2UE: Generating Unlearnable Examples from Text Descriptions GIT: A Generative Image-to-text Transformer for Vision and Language

Reference 42

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This paper cites Unlearnable 3d point clouds: Class-wise transformation is all you need.

T2UE: Generating Unlearnable Examples from Text Descriptions Unlearnable 3d point clouds: Class-wise transformation is all you need

Reference 43

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This paper cites Dcgan-based data augmentation for tomato leaf disease identification.

T2UE: Generating Unlearnable Examples from Text Descriptions Dcgan-based data augmentation for tomato leaf disease identification

Reference 44

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This paper cites Demystifying clip data.

T2UE: Generating Unlearnable Examples from Text Descriptions Demystifying clip data

Reference 45

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This paper cites Robust contrastive language-image pretraining against data poisoning and backdoor attacks.

T2UE: Generating Unlearnable Examples from Text Descriptions Robust contrastive language-image pretraining against data poisoning and backdoor attacks

Reference 46

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This paper cites Data poisoning attacks against multimodal encoders.

T2UE: Generating Unlearnable Examples from Text Descriptions Data poisoning attacks against multimodal encoders

Reference 47

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Observation d35b5d58-3675-45d9-8701-9a2e8c71746a · outbound

This paper cites How far are we from true unlearnability? In ICLR, 2025.

T2UE: Generating Unlearnable Examples from Text Descriptions How far are we from true unlearnability? In ICLR, 2025

Reference 48

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Observation 90db0ce1-27bd-4c42-9197-5f2c93b63950 · outbound

This paper cites From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions.

T2UE: Generating Unlearnable Examples from Text Descriptions From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions

Reference 49

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Observation 84d8b2b8-ec25-4703-9e4b-3fae417adca3 · outbound

This paper cites Availability attacks create shortcuts.

T2UE: Generating Unlearnable Examples from Text Descriptions Availability attacks create shortcuts

Reference 50

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Observation d35a8cf9-fcd8-4c5c-b6fc-185f256f27a0 · outbound

This paper cites Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks.

T2UE: Generating Unlearnable Examples from Text Descriptions Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks

Reference 51

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This paper cites mixup: Beyond Empirical Risk Minimization.

T2UE: Generating Unlearnable Examples from Text Descriptions mixup: Beyond Empirical Risk Minimization

Reference 52

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This paper cites Unlearnable clusters: Towards label-agnostic unlearnable examples.

T2UE: Generating Unlearnable Examples from Text Descriptions Unlearnable clusters: Towards label-agnostic unlearnable examples

Reference 53

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