katelyn98/CorruptionRobustness
We investigated corruption robustness across different architectures including Convolutional Neural Networks, Vision Transformers, and the MLP-Mixer.
This project investigates how well different image recognition models, like Convolutional Neural Networks and Vision Transformers, perform when images are corrupted with noise or distortions. It takes various pre-trained image classification models as input and evaluates their resilience to common image corruptions. Machine learning researchers and practitioners working on robust computer vision systems would find this useful.
No commits in the last 6 months.
Use this if you are developing or evaluating image recognition systems and need to understand their reliability under real-world, imperfect conditions.
Not ideal if you are looking for a tool to apply image corruptions or train models, as this project focuses on analysis rather than practical application.
Stars
16
Forks
2
Language
Jupyter Notebook
License
—
Category
Last pushed
Oct 28, 2021
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/katelyn98/CorruptionRobustness"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
namkoong-lab/dro
A package of distributionally robust optimization (DRO) methods. Implemented via cvxpy and PyTorch
neu-autonomy/nfl_veripy
Formal Verification of Neural Feedback Loops (NFLs)
THUDM/grb
Graph Robustness Benchmark: A scalable, unified, modular, and reproducible benchmark for...
MinghuiChen43/awesome-trustworthy-deep-learning
A curated list of trustworthy deep learning papers. Daily updating...
ADA-research/VERONA
A lightweight Python package for setting up robustness experiments and to compute robustness...