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adversarial-example---fgsm

Crafting adversarial example for semantic segmentaion model using FGSM.

allign_jdot

Alligned Joint distribution optimal transport

ap_derived

This repository contains the R codes of the Classification of Attribute profiles from the derived features for Urban cover classificationn

autoencoder_noisylabels

This repository contains the code of supervised and unsupervised branch of autoencoder to avoid overfitting deep neural networks to the noisy labels

awesome-learning-with-label-noise

A curated list of resources for Learning with Noisy Labels

awesome-learning-with-noisy-labels

Related works for the learning with noisy labels

awesome-transfer-learning

Best transfer learning and domain adaptation resources (papers, tutorials, datasets, etc.)

cleot

Source code for the "Entropic optimal transport loss for learning deep neural networks under label noise"

co-teaching

NIPS'18: Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels

convolutional-neural-networks

Documents used to publish on Towards Data Science

coursera

da

domain adaption survey papers

deepjdot

Implementation of DeepJDOT in Keras

eccv-aljdort

gmllib

Machine Learning Library

hsic_fs

This repository contains the matlab codes of Sparse HSIC Feature Selection Method

keras-dl-benchmark

keras-resnet

Residual networks implementation using Keras-1.0 functional API

pot

Python Optimal Transport library

prff

This repository contains the codes of Pseudo Random Fourier Features for Approximating RBF kernel

robust-conditional-gan

Code for reproducing results from our paper, Robustness of conditional GANs to noisy labels, NIPS 2018

sjdot

ubs_m2_dl

Materials for the deep learning course

vat

Implementation of VAT (Virtual Adversarial Training) on keras.

war

This repository contains the pytroch code to reproduce the results the paper "Wasserstein Adversarial Regularization for Learning with Label Noise"

woodyfeatures

This repository contains the R codes used for classification of woody features from pre-computed attribute profiles