The official implementation of ECCV'24 paper "To Generate or Not? Safety-Driven Unlearned Diffusion Models Are Still Easy To Generate Unsafe Images ... For Now". This work introduces one fast and effective attack method to evaluate the harmful-content generation ability of safety-driven unlearned diffusion models.

EasyRobust: an Easy-to-use library for state-of-the-art Robust Computer Vision Research with PyTorch.

Defending Against Misinformation Attacks in Open-Domain Question Answering

COBOL-like programming language that compiles to C++. With serious dinosaurs with neckties and briefcases 🦕💼

A unified evaluation framework for large language models

Reachability analysis for closed-loop control systems in Julia

Public repository of our assessment work in missing views for EO applications

[CVPR 2024] The official implementation of paper "synthesize, diagnose, and optimize: towards fine-grained vision-language understanding"

A Enterprise-Level API Framework based on NestJS, built for scaleability, best-practise and robustness. Based on DDD, Onion, Clean and Hexagonal Architecture

Out-of-distribution detection, robustness, and generalization resources. The repository contains a professionally curated list of papers, tutorials, books, videos, articles and open-source libraries etc

Tools for assessing clustering robustness

PyBullet CartPole and Quadrotor environments—with CasADi symbolic a priori dynamics—for learning-based control and RL

[IVS'24] UniBEV: the official implementation of UniBEV

Tunable sorting for responsive robustness and beyond!

official code for Customizable Embodied Multi-modal Perturbations for SLAM Robustness Benchmarking

Official code for "Enabling Uncertainty Estimation in Iterative Neural Networks" (ICML 2024)

A curated list of trustworthy deep learning papers. Daily updating...

A curated list of papers in Test-time Adaptation, Test-time Training and Source-free Domain Adaptation

ST-SSL (STSSL): Spatio-Temporal Self-Supervised Learning for Traffic Flow Forecasting/Prediction

MUAD: Multiple Uncertainties for Autonomous Driving, a benchmark for multiple uncertainty types and tasks [BMVC 2022]