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.
Official PyTorch implementation of the paper "Robust Training for Speaker Verification against Noisy Labels" in INTERSPEECH 2023.
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
A benchmark for instance segmentation on the long-tailed and noisy dataset.
Code for "From Instance to Metric Calibration: A Unified Framework for Open-World Few-Shot Learning" in TPAMI 2023.
[CVPR 2023] Official Implementation of "C-SFDA: A Curriculum Learning Aided Self-Training Framework for Efficient Source Free Domain Adaptation""
Implementation of Noisy Prediction Calibration (NPC) in Tensorflow
Code associated to the article "Multi-annotator Deep Learning: A Probabilistic Framework for Classification"
Re-implementation of the paper titled "Noise against noise: stochastic label noise helps combat inherent label noise" from ICLR 2021.
A Tensorflow (Keras) implementation of Peer loss functions for classification with noisy labels.
Cifar with Noisy from Human or Synthesis
A Label Studio plugin with InstanceGM for improving data labels for machine learning with machine learning
Code for the KDD-2023 paper: Neural-Hidden-CRF: A Robust Weakly-Supervised Sequence Labeler
Hypernetwork-Ensemble Learning of Segmentation Probability for Medical Image Segmentation with Ambiguous Labels
Noise Robust Learning with Hard Example Aware for Pathological Image classification
Official code of "No Regret Sample Selection with Noisy Labels"
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