Out-of-Distribution Generalization
73 papers with code • 0 benchmarks • 0 datasets
Benchmarks
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Libraries
Use these libraries to find Out-of-Distribution Generalization models and implementationsMost implemented papers
Open Graph Benchmark: Datasets for Machine Learning on Graphs
snap-stanford/ogb
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NeurIPS 2020
We present the Open Graph Benchmark (OGB), a diverse set of challenging and realistic benchmark datasets to facilitate scalable, robust, and reproducible graph machine learning (ML) research.
Invariant Risk Minimization
facebookresearch/InvariantRiskMinimization
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We introduce Invariant Risk Minimization (IRM), a learning paradigm to estimate invariant correlations across multiple training distributions.
Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics
allenai/cartography
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EMNLP 2020
Experiments across four datasets show that these model-dependent measures reveal three distinct regions in the data map, each with pronounced characteristics.
Out-of-Distribution Generalization via Risk Extrapolation (REx)
capybaralet/REx_code_release
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Distributional shift is one of the major obstacles when transferring machine learning prediction systems from the lab to the real world.
Radon cumulative distribution transform subspace modeling for image classification
rohdelab/rcdt_ns_classifier
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We present a new supervised image classification method applicable to a broad class of image deformation models.
Synbols: Probing Learning Algorithms with Synthetic Datasets
ElementAI/baal
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NeurIPS 2020
Progress in the field of machine learning has been fueled by the introduction of benchmark datasets pushing the limits of existing algorithms.
MUTANT: A Training Paradigm for Out-of-Distribution Generalization in Visual Question Answering
tejasG53/vqa_mutant
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EMNLP 2020
In this paper, we present MUTANT, a training paradigm that exposes the model to perceptually similar, yet semantically distinct mutations of the input, to improve OOD generalization, such as the VQA-CP challenge.
Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalization
Recently there has been increased interest in semi-supervised classification in the presence of graphical information.
Goal Misgeneralization in Deep Reinforcement Learning
jbkjr/train-procgen-pytorch
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We study goal misgeneralization, a type of out-of-distribution generalization failure in reinforcement learning (RL).
Quantifying and Improving Transferability in Domain Generalization
facebookresearch/DomainBed
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NeurIPS 2021
We then prove that our transferability can be estimated with enough samples and give a new upper bound for the target error based on our transferability.
