pxiangwu/PLC
ICLR 2021, "Learning with feature-dependent label noise: a progressive approach"
When training image classification models, this project helps researchers and machine learning engineers overcome the problem of inaccurate labels in their datasets. It takes a dataset where some image labels might be wrong and outputs a more robust classification model. This is for machine learning researchers and practitioners who work with real-world image datasets that often contain noisy or incorrect labels.
No commits in the last 6 months.
Use this if you are developing image classification models and suspect your training data contains errors or inconsistencies in its labels.
Not ideal if your dataset is perfectly clean with no label noise, or if you are working with non-image data.
Stars
46
Forks
8
Language
Python
License
—
Category
Last pushed
Oct 29, 2022
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/pxiangwu/PLC"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
AdaptiveMotorControlLab/CEBRA
Learnable latent embeddings for joint behavioral and neural analysis - Official implementation of CEBRA
theolepage/sslsv
Toolkit for training and evaluating Self-Supervised Learning (SSL) frameworks for Speaker...
PaddlePaddle/PASSL
PASSL包含 SimCLR，MoCo v1/v2，BYOL，CLIP，PixPro，simsiam, SwAV, BEiT，MAE 等图像自监督算法以及 Vision...
YGZWQZD/LAMDA-SSL
30 Semi-Supervised Learning Algorithms
ModSSC/ModSSC
ModSSC: A Modular Framework for Semi Supervised Classification