Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation | Paper Citation Record

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This paper cites Generalizing to unseen domains via distribution matching.

Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Generalizing to unseen domains via distribution matching

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This paper cites Improving out-of-distribution generalization via multi-task self-supervised pretraining.

Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Improving out-of-distribution generalization via multi-task self-supervised pretraining

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Towards understanding sharpness-aware minimization

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain Generalization

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Objectnet: A large-scale bias-controlled dataset for pushing the limits of object recognition models

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation An empirical comparison of voting classification algorithms: Bagging, boosting, and variants

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This paper cites A human-centered evaluation of a deep learning system deployed in clinics for the detection of diabetic retinopathy.

Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation A human-centered evaluation of a deep learning system deployed in clinics for the detection of diabetic retinopathy

Reference 7

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Recognition in terra incognita

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation On the Opportunities and Risks of Foundation Models

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Bagging predictors

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Random forests

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Language models are few-shot learners

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Swad: Domain generalization by seeking flat minima

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Exploiting hierarchical context on a large database of object categories

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Imagenet: A large-scale hierarchical image database

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Sharp minima can generalize for deep nets

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation The Role of Pretrained Representations for the OOD Generalization of Reinforcement Learning Agents

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation The pascal visual object classes (voc) challenge

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Unbiased metric learning: On the utilization of multiple datasets and web images for softening bias

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Sharpness-Aware Minimization for Efficiently Improving Generalization

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Linear mode connectivity and the lottery ticket hypothesis

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation A decision-theoretic generalization of on-line learning and an application to boosting

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Unsupervised domain adaptation by backpropagation

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Domain-adversarial training of neural networks

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Shortcut learning in deep neural networks

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation In Search of Lost Domain Generalization

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Stochastic Weight Averaging Revisited

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Stochastic Weight Averaging in Parallel: Large-Batch Training that Generalizes Well

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Momentum contrast for unsupervised visual representation learning

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Deep residual learning for image recognition

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation What shapes feature representations? exploring datasets, architectures, and training

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation beta- VAE : Learning basic visual concepts with a constrained variational framework

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Denoising diffusion probabilistic models

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Simplifying neural nets by discovering flat minima

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Unresolved cited work

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Adversarial examples are not bugs, they are features

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Averaging Weights Leads to Wider Optima and Better Generalization

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Scaling up visual and vision-language representation learning with noisy text supervision

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation When Do Flat Minima Optimizers Work?

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Generalization in anti-causal learning

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Disentangling by factorising

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Big transfer (bit): General visual representation learning

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Learning multiple layers of features from tiny images, 2009

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Asam: Adaptive sharpness-aware minimization for scale-invariant learning of deep neural networks

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Gradient-based learning applied to document recognition

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Deeper, broader and artier domain generalization

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Cross-domain adaptive clustering for semi-supervised domain adaptation

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Few-shot domain adaptation with polymorphic transformers

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Domain invariant and class discriminative feature learning for visual domain adaptation

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Learning transferable features with deep adaptation networks

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Deep transfer learning with joint adaptation networks

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Reference 58

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Reference 59

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Reference 60

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Reference 61

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Reference 62

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Reference 63

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Reading digits in natural images with unsupervised feature learning

Reference 64

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Reference 65

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This paper cites What is being transferred in transfer learning? Advances in neural information processing systems , 33:512--523, 2020.

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Reference 66

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning

Reference 67

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Can you trust your model's uncertainty? evaluating predictive uncertainty under dataset shift

Reference 68

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Moment matching for multi-source domain adaptation

Reference 69

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Visda: The visual domain adaptation challenge, 2017

Reference 70

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Learning transferable visual models from natural language supervision

Reference 71

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Diverse Weight Averaging for Out-of-Distribution Generalization

Reference 72

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Ensembles of locally independent prediction models

Reference 73

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Optimal Representations for Covariate Shift

Reference 74

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Labelme: a database and web-based tool for image annotation

Reference 75

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Out-of-domain detection based on generative adversarial network

Reference 76

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Ensemble learning: A survey

Reference 77

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Semi-supervised domain adaptation via minimax entropy

Reference 78

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Maximum classifier discrepancy for unsupervised domain adaptation

Reference 79

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Toward causal representation learning

Reference 80

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Visual Representation Learning Does Not Generalize Strongly Within the Same Domain

Reference 81

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation The pitfalls of simplicity bias in neural networks

Reference 82

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Return of Frustratingly Easy Domain Adaptation

Reference 83

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Intriguing properties of neural networks

Reference 84

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Evading the simplicity bias: Training a diverse set of models discovers solutions with superior ood generalization

Reference 85

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Few-shot domain adaptation by causal mechanism transfer

Reference 86

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Adversarial discriminative domain adaptation

Reference 87

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Reference 88

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Deep hashing network for unsupervised domain adaptation

Reference 89

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Multimodal Self-Supervised Learning of General Audio Representations

Reference 90

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Robustness to corruption in pre-trained Bayesian neural networks

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

Reference 93

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Extrapolation and Spectral Bias of Neural Nets with Hadamard Product: a Polynomial Net Study

Reference 94

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation d-sne: Domain adaptation using stochastic neighborhood embedding

Reference 96

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Billion-scale semi-supervised learning for image classification

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Improved ood generalization via adversarial training and pretraing

Reference 98

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation CoCa: Contrastive Captioners are Image-Text Foundation Models

Reference 99

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Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation Understanding deep learning (still) requires rethinking generalization

Reference 100

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