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.
Robust learning on ISIC 2018, based on Learning with Noisy Labels via Sparse Regularization (ICCV 2021).
Implementations of different loss-correction techniques to help deep models learn under class-conditional label noise.
Discovering Premature Replacements in Predictive Maintenance Time-to-Event Data
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
Performed weakly supervised learning on CIFAR-10 images with noisy labels using convolutional neural networks (CNN).
A Label Studio plugin with InstanceGM for improving data labels for machine learning with machine learning
Cifar with Noisy from Human or Synthesis
A small experiment on classification with noisy labels
Exploring Parity Challenges in Reinforcement Learning through Curriculum Learning with Noisy Labels
Shopee Code League 2020 image competition 7th place solution
CNN Image classification for Cifar 10 & Cifar 100 dataset using PyTorch
Code associated to the article "Who knows best? Intelligent Crowdworker Selection via Deep Learning"
Re-implementation of the paper titled "Noise against noise: stochastic label noise helps combat inherent label noise" from ICLR 2021.
Implementation of Noisy Prediction Calibration (NPC) in Tensorflow
A curated list of awesome Weak-Supervision-Sequence-Labeling (WSSL) papers, methods & resources.
A benchmark for instance segmentation on the long-tailed and noisy dataset.
A tool for automatically labelling discharge summaries into disease categories.
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