Out-of-Distribution Generalization

73 papers with code • 0 benchmarks • 0 datasets

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Use these libraries to find Out-of-Distribution Generalization models and implementations

Most implemented papers

Open Graph Benchmark: Datasets for Machine Learning on Graphs

snap-stanford/ogb 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 5 Jul 2019

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 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 2 Mar 2020

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 7 Apr 2020

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 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 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

opallab/icml-21-graph-conv 13 Feb 2021

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 28 May 2021

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 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.