RumengYi

CIR

noisy_label_understanding_utilizing

ICML 2019: Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels

ACT_AAAI20

code for AAAI 2020 paper "ACT"

deep_Mahalanobis_detector

Code for the paper "A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks".

DLDL

Deep Label Distribution Learning with Label Ambiguity

GraphNet

GraphNet: Learning Image Pseudo Annotations for Weakly-Supervised Semantic Segmentation, in ACM MM 2018 (oral)

keras-cam

Keras implementation of class activation mapping

LabelNoiseCorrection

Official Implementation of ICML 2019 Unsupervised label noise modeling and loss correction

learning-to-reweight-examples

PyTorch Implementation of the paper Learning to Reweight Examples for Robust Deep Learning

MLNT

Meta-Learning based Noise-Tolerant Training

models

Models and examples built with TensorFlow

PENCIL

PyTorch implementation of Probabilistic End-to-end Noise Correction for Learning with Noisy Labels, CVPR 2019.

psa

Learning Pixel-level Semantic Affinity with Image-level Supervision for Weakly Supervised Semantic Segmentation, CVPR 2018

pytorch-deeplab-xception

DeepLab v3+ model in PyTorch. Support different backbones.

pytorch-fcn

PyTorch Implementation of Fully Convolutional Networks. (Training code to reproduce the original result is available.)

pytorch_CAM

The reimplementation of Class Activation Map in pytorch

VGG16CAM-keras

Keras implementation of the VGG16-CAM model