generalization

Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习

Release of CIFAR-10.1, a new test set for CIFAR-10.

[CVPR 2021] SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration

A Compositional Object-Based Approach to Learning Physical Dynamics

A new test set for ImageNet

[ICML 2020] PyTorch Code for "One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control"

CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning

Interpretable and Generalizable Person Re-Identification with Query-Adaptive Convolution and Temporal Lifting

Javascript Generalized Parser Combinators

Official implementation of the NeurIPS'2020 paper 'Self-Adaptive Training: beyond Empirical Risk Minimization'

PGDrive: an open-ended driving simulator with infinite scenes from procedural generation

Implementation of the paper Recurrent Independent Mechanisms (https://arxiv.org/pdf/1909.10893.pdf)

Code for 3D Reconstruction of Novel Object Shapes from Single Images paper

Awesome Domain Adaptation Python Toolbox

Code of paper: "DropAttack: A Masked Weight Adversarial Training Method to Improve Generalization of Neural Networks"

The official repository for our paper "The Devil is in the Detail: Simple Tricks Improve Systematic Generalization of Transformers". We significantly improve the systematic generalization of transformer models on a variety of datasets using simple tricks and careful considerations.

Simple (but often Strong) Baselines for POMDPs in PyTorch

This repository contains code to replicate the experiments given in NeurIPS 2019 paper "One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers"

Computing various norms/measures on over-parametrized neural networks

Implements sharpness-aware minimization (https://arxiv.org/abs/2010.01412) in TensorFlow 2.

On the decision boundary of deep neural networks

Awesome list for Neural Network Optimization methods.

Official code of "Discovering Invariant Rationales for Graph Neural Networks" (2022 ICLR)

Improving generalization by controlling label-noise information in neural network weights.

Winning Solution of the NeurIPS 2020 Competition on Predicting Generalization in Deep Learning

Compositional Generalization in NLP. A roadmap.

Source code and dataset for the CCKS201 paper "On Robustness and Bias Analysis of BERT-based Relation Extraction"

The official repository for our paper "Are Neural Nets Modular? Inspecting Functional Modularity Through Differentiable Weight Masks". We develop a method for analyzing emerging functional modularity in neural networks based on differentiable weight masks and use it to point out important issues in current-day neural networks.

ML/DL/RL paper notes

Implementation and Benchmark Splits to study Out-of-Distribution Generalization in Deep Metric Learning.