Adversarial training on Noisy Datasets
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Updated
Dec 29, 2022 - Python
Adversarial training on Noisy Datasets
The objective of this project is to be able to discriminate from 4 of the most common leaf disease that infect cassava crops.
Implementations of different loss-correction techniques to help deep models learn under class-conditional label noise.
The official PyTorch code for WACV'21 Paper "Noisy Concurrent Training for Efficient Learning under Label Noise"
Deep Learning for Suicide and Depression Identification with Unsupervised Label Correction. 2021
Cifar with Noisy from Human or Synthesis
A Label Studio plugin with InstanceGM for improving data labels for machine learning with machine learning
Code associated to the article "Who knows best? Intelligent Crowdworker Selection via Deep Learning"
CNN Image classification for Cifar 10 & Cifar 100 dataset using PyTorch
Implementation of Noisy Prediction Calibration (NPC) in Tensorflow
Re-implementation of the paper titled "Noise against noise: stochastic label noise helps combat inherent label noise" from ICLR 2021.
A benchmark for instance segmentation on the long-tailed and noisy dataset.
A tool for automatically labelling discharge summaries into disease categories.
Training with Data Annotated by Multipe Error-prone Annotators
A Tensorflow (Keras) implementation of Peer loss functions for classification with noisy labels.
Code associated to the article "Multi-annotator Deep Learning: A Probabilistic Framework for Classification"
Hypernetwork-Ensemble Learning of Segmentation Probability for Medical Image Segmentation with Ambiguous Labels
Noise Robust Learning with Hard Example Aware for Pathological Image classification
A TensorFlow implementation of "Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels"
Official code of "No Regret Sample Selection with Noisy Labels"
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