Create and manage interactive keyboard shortcut cheatsheets.

Figures & code from the paper "Shortcut Learning in Deep Neural Networks" (Nature Machine Intelligence 2020)

[Nature Medicine] The Limits of Fair Medical Imaging AI In Real-World Generalization

Frequency Shortcuts in Neural Networks

How to master your passwords using iCloud Keychain.

TextAdaIN: Paying Attention to Shortcut Learning in Text Recognizers

This is the official code for CoLLAs 2022 paper, "InBiaseD: Inductive Bias Distillation to Improve Generalization and Robustness through Shape-awareness"

Augmentation for CV using frequency shortcuts

Study on the effect of masking the ROI in medical images to evaluate potential bias/shortcuts in datasets

GitHub Repository for "Efficient Unsupervised Shortcut Learning Detection and Mitigation in Transformers" presented in ICCV 2025.

My dissertation project proposing novel Hybrid CNN–Transformer A/B models and scalable segmentation-free guidance modules for investigating shortcut-learning mitigation in brain tumour MRI classification when pixel-level masks are unavailable.

Shortcut Learning in Financial Text Mining @ IEEE BigData 2022

Reproduction code + artifacts for “When Do Deep Ensembles Improve Robustness to Spurious Correlations?” (TU Delft, 2026).

A deep learning method using cross-domain regularization to mitigate shortcut learning

This repository contains the code for the paper "Mitigating Shortcut Learning via Feature Disentanglement in Medical Imaging: A Benchmark Study".

Get a fast overview of shortcuts for (almost) everything.

Shortcut Wizard is a desktop app that helps professionals and students organize and access application shortcuts effortlessly, boosting productivity with ease.

Published in the Journal of the American Medical Informatics Association JAMIA Open (2026). DOI: 10.1093/jamiaopen/ooaf177. Auditing shortcut learning and misclassification in AI breast cancer genomic subtyping using TCGA-BRCA. Harvard Dataverse: 10.7910/DVN/BALJNT

Multi-branch CNN for potato leaf disease classification (96.4% on 7 classes, 5 geographic sources) audited with six XAI techniques (Grad-CAM, IG, occlusion, β-routing analysis, k-NN, and counterfactual mask-flip) to distinguish genuine disease recognition from shortcut learning.