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Advances In Label Noise Learning ⭐ 465

A curated (most recent) list of resources for Learning with Noisy Labels

Co Learning Learning From Noisy Labels With Self Supervision ⭐ 107

The official implementation of the ACM MM'2021 paper Co-learning: Learning from noisy labels with self-supervision.

Clean Net ⭐ 62

Tensorflow source code for "CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise"

Active Passive Losses ⭐ 37

Code for ICML2020 paper [“Normalized Loss Functions for Deep Learning with Noisy Labels"] https://arxiv.org/abs/2006.13554

Hard Sample Aware Noise Robust Learning forHistopathology Image Classification

Improving Mean Absolute Error Against Cce ⭐ 21

Mean Absolute Error Does Not Treat Examples Equally and Gradient Magnitude’s Variance Matters

Analytics Vidhya Game Of Deep Learning Hackathon ⭐ 7

Contains my experiments for the Game of Deep Learning Hackathon conducted by Analytics Vidhya

Derivativemanipulation ⭐ 5

In the context of Deep Learning: What is the right way to conduct example weighting? How do you understand loss functions and so-called theorems on them?

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