Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations | Paper Citation Record

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This paper cites Domain generalization by rejecting extreme augmentations.

Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations Domain generalization by rejecting extreme augmentations

Reference 1

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This paper cites A data- augmentation is worth a thousand samples: Analytical mo- ments and sampling-free training.

Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations A data- augmentation is worth a thousand samples: Analytical mo- ments and sampling-free training

Reference 2

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This paper cites Recognition in terra incognita, 2018.

Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations Recognition in terra incognita, 2018

Reference 3

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Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations On tiny episodic memories in continual learning

Reference 4

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This paper cites Just pick a sign: Optimizing deep multitask models with gradi- ent sign dropout.

Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations Just pick a sign: Optimizing deep multitask models with gradi- ent sign dropout

Reference 5

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This paper cites Peer pressure: Model-to-model regularization for single source domain generalization.

Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations Peer pressure: Model-to-model regularization for single source domain generalization

Reference 6

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This paper cites AutoAugment: Learning Augmentation Policies from Data.

Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations AutoAugment: Learning Augmentation Policies from Data

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This paper cites Randaugment: Practical automated data augmentation with a reduced search space.

Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations Randaugment: Practical automated data augmentation with a reduced search space

Reference 8

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This paper cites The mnist database of handwritten digit images for machine learning research.

Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations The mnist database of handwritten digit images for machine learning research

Reference 9

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This paper cites Craft- ing distribution shifts for validation and training in single source domain generalization, 2024.

Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations Craft- ing distribution shifts for validation and training in single source domain generalization, 2024

Reference 10

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This paper cites Adversarially adaptive normal- ization for single domain generalization.

Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations Adversarially adaptive normal- ization for single domain generalization

Reference 11

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This paper cites Unbiased met- ric learning: On the utilization of multiple datasets and web images for softening bias.

Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations Unbiased met- ric learning: On the utilization of multiple datasets and web images for softening bias

Reference 12

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Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations Unsupervised domain adaptation by backpropagation

Reference 13

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Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations Domain-adversarial training of neural networks

Reference 14

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This paper cites How Much Data Are Augmentations Worth? An Investigation into Scaling Laws, Invariance, and Implicit Regularization.

Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations How Much Data Are Augmentations Worth? An Investigation into Scaling Laws, Invariance, and Implicit Regularization

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This paper cites An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks.

Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks

Reference 16

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This paper cites Goodfellow, Jonathon Shlens, and Christian Szegedy.

Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations Goodfellow, Jonathon Shlens, and Christian Szegedy

Reference 17

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This paper cites Out-of-distribution forgetting: vulnerability of continual learning to intra-class distribution shift.

Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations Out-of-distribution forgetting: vulnerability of continual learning to intra-class distribution shift

Reference 18

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This paper cites Deep residual learning for image recognition.

Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations Deep residual learning for image recognition

Reference 19

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This paper cites Data augmentation revisited: Rethinking the distribution gap between clean and augmented data, 2019.

Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations Data augmentation revisited: Rethinking the distribution gap between clean and augmented data, 2019

Reference 20

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This paper cites Averaging Weights Leads to Wider Optima and Better Generalization.

Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations Averaging Weights Leads to Wider Optima and Better Generalization

Reference 21

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This paper cites Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, and Raia Hadsell.

Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, and Raia Hadsell

Reference 22

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Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations Similarity of neural network representations revisited

Reference 23

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This paper cites Continual learning with weight interpo- lation.

Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations Continual learning with weight interpo- lation

Reference 24

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Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations Im- agenet classification with deep convolutional neural networks

Reference 25

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This paper cites Le Cun, B.

Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations Le Cun, B

Reference 26

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Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations Deeper, broader and artier domain generaliza- tion

Reference 27

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Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations Domain generalization for medical imaging classification with linear-dependency regularization

Reference 28

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Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations Unresolved cited work

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Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations Gradient episodic memory for continual learning

Reference 30

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Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations Continual federated learning based on knowledge distillation

Reference 31

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Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations Weighted Ensemble Models Are Strong Continual Learners

Reference 32

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Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations Unresolved cited work

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This paper cites Learning to learn single domain generalization.

Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations Learning to learn single domain generalization

Reference 34

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Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations Diverse weight averaging for out-of-distribution generaliza- tion

Reference 35

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Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations Experience replay for continual learning

Reference 36

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Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations Complementary Learning for Overcoming Catastrophic Forgetting Using Experience Replay

Reference 37

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Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations Continual learning with scaled gradient projection

Reference 38

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Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations Deep hashing network for unsupervised domain adaptation

Reference 39

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Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations Generalizing to unseen domains via adversarial data augmentation

Reference 40

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Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations Meta convolutional neural networks for single domain generalization

Reference 41

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Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations FOSTER: Feature Boosting and Compression for Class-Incremental Learning

Reference 42

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Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations A Comprehensive Survey of Continual Learning: Theory, Method and Application

Reference 43

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Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations Learning to diversify for single domain generalization

Reference 44

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This paper cites Learning structured sparsity in deep neural networks,.

Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations Learning structured sparsity in deep neural networks,

Reference 45

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Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations Unresolved cited work

Reference 46

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This paper cites Simde: A simple domain expansion approach for single-source domain generalization.

Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations Simde: A simple domain expansion approach for single-source domain generalization

Reference 47

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This paper cites Gradient surgery for multi- task learning.

Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations Gradient surgery for multi- task learning

Reference 48

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This paper cites Maximum-entropy adversarial data augmentation for im- proved generalization and robustness.

Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations Maximum-entropy adversarial data augmentation for im- proved generalization and robustness

Reference 49

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This paper cites Advst: Revisiting data augmentations for single domain generaliza- tion.

Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations Advst: Revisiting data augmentations for single domain generaliza- tion

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:20:41.516334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers