(CVPR 2021) Code for our method RePRI for Few-Shot Segmentation. Paper at http://arxiv.org/abs/2012.06166

Domain Generalization via Gradient Surgery

Simple but high-performing method for learning a policy of test-time augmentation

[NeurIPS 2022] The official repository of Expression Learning with Identity Matching for Facial Expression Recognition

Gpu accelerated vahadane stain normalization for Digital Pathology workflows.

NeurIPS 2024 (spotlight): A Textbook Remedy for Domain Shifts Knowledge Priors for Medical Image Analysis

LAMDA: Label Matching Deep Domain Adaptation - ICML 2021

ToyADMOS2: Another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions 🚗 🚃

Carloni, G., Tsaftaris, S. A., & Colantonio, S. (2024). CROCODILE: Causality aids RObustness via COntrastive DIsentangled LEarning @ MICCAI 2024 UNSURE Workshop

How to predict uncertainty of regression GNN models under target domain shifts?

Code for the paper "Addressing caveats of neural persistence with deep graph persistence".

Domain Adaptive and Fine-grained Anomaly Detection for Single-cell Sequencing Data and Beyond

Code related to the article "Reducing domain shift in synthetic data augmentation for semantic segmentation of 3D point clouds" submitted to the SMC 2022 conference

Official repository of our AILC CLiC-it 2023 paper When You Doubt, Abstain: A Study of Automated Fact-checking in Italian Under Domain Shift.

Pytorch implementation of Deep Generic Representations for Domain-Generalized Anomalous Sound Detection: https://arxiv.org/abs/2409.05035

This repository contains the code for the paper "Benchmarking Dependence Measures to Prevent Shortcut Learning in Medical Imaging".

Domain-shift Aware Meta-Learning for Domain Generalization in SER

A repository to demonstrate the domain shift between the different domains of the Office-31 dataset. The repository includes feature extraction from the raw images and then analyzing those feature vectors.

Repository dedicated to the studies of domain shift in the context of classification of OSCC histopathology images

I'm now interested in Learning to Adapt to Domain Shift