[NeurIPS 2025] This repo is official PyTorch implementation of the paper "Learning Dense Hand Contact Estimation from Imbalanced Data".
pytorch implementation of Shrinkage loss in our ECCV paper 2018: Deep regression tracking with shrinkage loss
Shortcut to Nowhere: Demystifying Deep Spurious Regression
compare the performance of cross entropy, focal loss, and dice loss in solving the problem of data imbalance
software vulnerability detection
dau is a Python package that implements Density-Aware Undersampling (DAU), a novel undersampling technique for handling imbalanced datasets.
Codes for paper titled "TC-Sniffer: A Transformer-CNN Bibranch Framework Leveraging Auxiliary VOCs for Few-Shot UBC Diagnosis via Electronic Noses"
Detection of dermoscopic structures for melanoma diagonsis
Dice loss for data-imbalanced NLP tasks
Membership Inference Attacks on Imbalanced Federated Learning setups
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