Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation | Paper Citation Record

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This paper cites Imagenet classification with deep convolutional neural networks.

Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation Imagenet classification with deep convolutional neural networks

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

Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation Learning transferable visual models from natural language supervision

Reference 2

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

Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 3

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This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 4

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This paper cites Attention is all you need.

Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation Attention is all you need

Reference 5

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This paper cites Meta-Reinforcement Learning Robust to Distributional Shift via Model Identification and Experience Relabeling.

Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation Meta-Reinforcement Learning Robust to Distributional Shift via Model Identification and Experience Relabeling

Reference 6

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This paper cites Adapting visual category models to new domains.

Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation Adapting visual category models to new domains

Reference 7

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This paper cites Measuring robustness to natural distribution shifts in image classification.

Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation Measuring robustness to natural distribution shifts in image classification

Reference 8

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This paper cites Parameter-free online test-time adaptation.

Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation Parameter-free online test-time adaptation

Reference 9

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This paper cites Test time adaptation via conjugate pseudo- labels.

Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation Test time adaptation via conjugate pseudo- labels

Reference 10

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This paper cites Test-time adaptation via self-training with nearest neighbor information.

Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation Test-time adaptation via self-training with nearest neighbor information

Reference 11

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This paper cites Tent: Fully test-time adaptation by entropy minimization.

Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation Tent: Fully test-time adaptation by entropy minimization

Reference 12

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This paper cites Delta: Degradation-free fully test-time adaptation.

Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation Delta: Degradation-free fully test-time adaptation

Reference 13

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This paper cites Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift.

Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift

Reference 14

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Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation Im- proving robustness against common corruptions by covariate shift adaptation

Reference 15

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This paper cites Pseudo-label : The simple and efficient semi-supervised learning method for deep neural networks.

Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation Pseudo-label : The simple and efficient semi-supervised learning method for deep neural networks

Reference 16

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This paper cites Ods: test-time adaptation in the presence of open-world data shift.

Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation Ods: test-time adaptation in the presence of open-world data shift

Reference 17

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This paper cites Note: Robust continual test-time adaptation against temporal correlation.

Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation Note: Robust continual test-time adaptation against temporal correlation

Reference 18

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This paper cites Towards stable test-time adaptation in dynamic wild world.

Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation Towards stable test-time adaptation in dynamic wild world

Reference 19

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This paper cites Learning with noisy labels.

Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation Learning with noisy labels

Reference 20

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This paper cites Making deep neural networks robust to label noise: A loss correction approach.

Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation Making deep neural networks robust to label noise: A loss correction approach

Reference 21

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This paper cites Clusterability as an alternative to anchor points when learning with noisy labels.

Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation Clusterability as an alternative to anchor points when learning with noisy labels

Reference 22

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This paper cites Domain adaptation with invariant representation learning: What transformations to learn? Advances in Neural Information Processing Systems, 34:24791–24803, 2021.

Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation Domain adaptation with invariant representation learning: What transformations to learn? Advances in Neural Information Processing Systems, 34:24791–24803, 2021

Reference 23

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This paper cites When Source-Free Domain Adaptation Meets Learning with Noisy Labels.

Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation When Source-Free Domain Adaptation Meets Learning with Noisy Labels

Reference 24

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

Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation Learning multiple layers of features from tiny images

Reference 25

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This paper cites Benchmarking neural network robustness to common corruptions and perturbations.

Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation Benchmarking neural network robustness to common corruptions and perturbations

Reference 26

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This paper cites Label Shift Adapter for Test-Time Adaptation under Covariate and Label Shifts.

Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation Label Shift Adapter for Test-Time Adaptation under Covariate and Label Shifts

Reference 27

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This paper cites Improving test-time adaptation via shift-agnostic weight regularization and nearest source prototypes.

Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation Improving test-time adaptation via shift-agnostic weight regularization and nearest source prototypes

Reference 28

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This paper cites Ttn: A domain-shift aware batch normalization in test-time adaptation.

Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation Ttn: A domain-shift aware batch normalization in test-time adaptation

Reference 29

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Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation Bayesian nonparametric federated learning of neural networks

Reference 30

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This paper cites Do CIFAR-10 Classifiers Generalize to CIFAR-10?.

Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation Do CIFAR-10 Classifiers Generalize to CIFAR-10?

Reference 31

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Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation Hospedales

Reference 32

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Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation Deep hashing network for unsupervised domain adaptation

Reference 33

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Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation Deep residual learning for image recognition

Reference 34

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Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation Neural networks: a comprehensive foundation

Reference 35

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Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation Leveraging proxy of training data for test-time adaptation

Reference 36

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Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation Smote: synthetic minority over-sampling technique

Reference 37

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Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation Exploratory undersampling for class-imbalance learning

Reference 38

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Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation Disentangling label distribution for long-tailed visual recognition

Reference 39

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Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation Long-tail learning via logit adjustment

Reference 40

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Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation Rlsbench: Domain adaptation under relaxed label shift

Reference 41

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This paper cites 80 million tiny images: A large data set for nonparametric object and scene recognition.

Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation 80 million tiny images: A large data set for nonparametric object and scene recognition

Reference 42

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This paper cites On pitfalls of test-time adaptation.

Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation On pitfalls of test-time adaptation

Reference 43

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Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation Pytorch: An imperative style, high-performance deep learning library

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Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation Deep Learning using Rectified Linear Units (ReLU)

Reference 45

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Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation Adam: A Method for Stochastic Optimization

Reference 46

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Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation Distilling the Knowledge in a Neural Network

Reference 47

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Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation Test-time classifier adjustment module for model-agnostic domain general- ization

Reference 48

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Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation Class relationship embedded learning for source-free unsupervised domain adaptation

Reference 49

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This paper cites Are anchor points really indispensable in label-noise learning? Advances in neural information processing systems, 32, 2019.

Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation Are anchor points really indispensable in label-noise learning? Advances in neural information processing systems, 32, 2019

Reference 50

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Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation Dual t: Reducing estimation error for transition matrix in label-noise learning

Reference 51

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This paper cites Dataset distillation by matching training trajectories.

Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation Dataset distillation by matching training trajectories

Reference 52

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This paper cites Universal test-time adaptation through weight ensembling, diversity weighting, and prior correction.

Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation Universal test-time adaptation through weight ensembling, diversity weighting, and prior correction

Reference 53

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

Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation An empirical study of training self-supervised vision transformers

Reference 54

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This paper cites mixed domain.

Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation mixed domain

Reference 55

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This paper cites This demonstrates the efficiency of DART’s intermediate-time training, significantly enhancing its scalability for practical use.

Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation This demonstrates the efficiency of DART’s intermediate-time training, significantly enhancing its scalability for practical use

Reference 56

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