nis-research/nn-frequency-shortcuts
Frequency Shortcuts in Neural Networks
This project helps computer vision researchers and practitioners understand how neural networks classify images, specifically by analyzing which image frequencies (like textures or shapes) they prioritize. It takes in trained image classification models and image datasets, then outputs metrics and visualizations that reveal whether the network relies on 'frequency shortcuts.' This insight helps those trying to build more robust and generalizable image classification systems.
No commits in the last 6 months.
Use this if you are a computer vision researcher or ML engineer investigating why your image classification models sometimes fail on new, unseen data and suspect they might be learning superficial patterns.
Not ideal if you are looking for a plug-and-play solution to directly improve model performance without needing to understand the underlying learning mechanisms.
Stars
21
Forks
1
Language
Python
License
MIT
Category
Last pushed
Nov 01, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/nis-research/nn-frequency-shortcuts"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
digantamisra98/Mish
Official Repository for "Mish: A Self Regularized Non-Monotonic Neural Activation Function" [BMVC 2020]
Sentdex/nnfs_book
Sample code from the Neural Networks from Scratch book.
itdxer/neupy
NeuPy is a Tensorflow based python library for prototyping and building neural networks
vzhou842/cnn-from-scratch
A Convolutional Neural Network implemented from scratch (using only numpy) in Python.
nicklashansen/rnn_lstm_from_scratch
How to build RNNs and LSTMs from scratch with NumPy.