zyh-uaiaaaa/Erasing-Attention-Consistency
Official implementation of the ECCV2022 paper: Learn From All: Erasing Attention Consistency for Noisy Label Facial Expression Recognition
This project helps researchers and developers improve the accuracy of facial expression recognition models, especially when trained with imperfect or mislabeled image data. It takes in a dataset of facial images with potentially noisy expression labels and outputs a more robust and accurate model capable of correctly identifying emotions. Scientists and engineers working on emotional AI, human-computer interaction, or psychological studies involving facial cues would find this beneficial.
No commits in the last 6 months.
Use this if you are training facial expression recognition models and suspect your image labels might contain errors or ambiguities, which can reduce your model's real-world performance.
Not ideal if your dataset is perfectly clean with no label noise, or if your primary interest is in general object classification rather than facial expressions specifically.
Stars
92
Forks
17
Language
Python
License
—
Category
Last pushed
Oct 04, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/zyh-uaiaaaa/Erasing-Attention-Consistency"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
adapt-python/adapt
Awesome Domain Adaptation Python Toolbox
corenel/pytorch-adda
A PyTorch implementation for Adversarial Discriminative Domain Adaptation
jindongwang/transferlearning
Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers,...
thuml/Transfer-Learning-Library
Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization
KaiyangZhou/Dassl.pytorch
A PyTorch toolbox for domain generalization, domain adaptation and semi-supervised learning.