Official repository of our work "Fairness meets Cross-Domain Learning: a Benchmark of Models and Metrics" published on IEEE Access

[TIP-2023] IEEE Trans.on Image Processing

code for "Learning Generalizable Part-based Feature Representation for 3D Point Clouds" (NeurIPS 2022)

A repository for federated domain generalization.

"Multi-Source Collaborative Gradient Discrepancy Minimization for Federated Domain Generalization", AAAI 2024, pytorch

An unofficial implementation of Frustratingly Simple Domain Generalization via Image Stylization

Project Page (PromptStyler, ICCV 2023)

Official pytorch implementation of the paper [Environment Agnostic Representation for Visual Reinforcement learning]

Proposed a novel method for image quality enhancement using diffusion models

Do Domain Generalization Methods Generalize Well? (Workshop on Machine Learning Safety at NeurIPS 2022).

EM-Net: Effective and Morphology-aware Network for Skin Lesion Segmentation

Gated Domain Units (GDU) aim to make your deep learning models robust against distribution shifts when applied in the real-world.

lint - Leveraging INTerpretability

Domain Adaptation methods on Office31

[BMVC2023] This is the official repo for Semi-Supervised Domain Generalization for Object Detection via Language-Guided Feature Alignment (BMVC2023)

This repository contains the code associated with the master's thesis "Invariance and Generalization for Classification" written by Linus Kühne at ETH Zurich in 2024.

Official PyTorch implementation of CVPRW 2022 paper "Attention Consistency on Visual Corruptions for Single-Source Domain Generalization"

Code for the control+shift paper, where a method for generating general datasets with a controllable amount of distribution shift was proposed. Presented at the ECCV'24 "Synthetic Data for Computer Vision" workshop.

Data loader and solution method for the DCASE 2024 Challenge Task1

[AAAI 2025] Official implementation of the paper "Exploring Semantic Consistency and Style Diversity for Domain Generalized Semantic Segmentation"