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52 search results found

Unsupervised Data Augmentation (UDA)

Autoaugment ⭐ 1,382

Unofficial implementation of the ImageNet, CIFAR 10 and SVHN Augmentation Policies learned by AutoAugment using pillow

Stochastic_depth ⭐ 443

Deep Networks with Stochastic Depth

Efficient Learning of Augmentation Policy Schedules

Imbalanced Semi Self ⭐ 393

[NeurIPS 2020] Semi-Supervision (Unlabeled Data) & Self-Supervision Improve Class-Imbalanced / Long-Tailed Learning

Datasetcondensation ⭐ 382

Dataset Condensation (ICLR21 and ICML21)

Cliquenet ⭐ 321

Convolutional Neural Networks with Alternately Updated Clique (to appear in CVPR 2018)

Glow Pytorch ⭐ 317

Simple, extendable, easy to understand Glow implementation in PyTorch

2.56%, 15.20%, 1.30% on CIFAR10, CIFAR100, and SVHN https://arxiv.org/abs/1708.04552

Adversarially Learned Inference

Fusenet ⭐ 233

Deep fusion project of deeply-fused nets, and the study on the connection to ensembling

Virtual adversarial training with Tensorflow

Ssl_bad_gan ⭐ 157

Good Semi-Supervised Learning That Requires a Bad GAN

Deeply-supervised Nets

Code for reproducing ICT (published in Neural Networks 2022, and in IJCAI 2019)

Deep_mahalanobis_detector ⭐ 118

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

Metaqnn ⭐ 115

Binaryconnect ⭐ 79

Source code for ``Neural Networks with Few Multiplications'' published at ICLR 2016

EnAET: Self-Trained Ensemble AutoEncoding Transformations for Semi-Supervised Learning

Code for the "Discriminative Regularization for Generative Models" paper.

EnAET: Self-Trained Ensemble AutoEncoding Transformations for Semi-Supervised Learning

Gibbsnet ⭐ 57

Implementation of paper "GibbsNet: Iterative Adversarial Inference for Deep Graphical Models" in PyTorch

Cuda Convnet ⭐ 43

started from Alex's code on google code

Vat_chainer ⭐ 36

Implementation of Virtual adversarial training with chainer

Informative Outlier Mining ⭐ 35

We propose a theoretically motivated method, Adversarial Training with informative Outlier Mining (ATOM), which improves the robustness of OOD detection to various types of adversarial OOD inputs and establishes state-of-the-art performance.

Wide Residual Network ⭐ 35

Wide-residual network implementations. Best result for cifar10(97.12%), cifar100(84.12%), and other kaggle challenges

Cac Openset ⭐ 34

Code for Class Anchor Clustering loss for training a distance-based open set classifier

Residual Binarized Neural Network

Gan Manifold Regularization ⭐ 28

This is the code we used in our paper accepted at ICLR 2018

Based on the CIFAR10 example on TensorFlow. Reconfiguring it to run my own images and data.

Amp Regularizer ⭐ 25

Code for our paper "Regularizing Neural Networks via Adversarial Model Perturbation", CVPR2021

Residual Flow ⭐ 25

An implementation of the Residual Flow algorithm for out-of-distribution detection.

code for "DADA: Deep Adversarial Data Augmentation for Extremely Low Data Regime Classification"

Theano Xnor Net ⭐ 23

Theano implementation of XNOR-Net

Epsilonresnet ⭐ 17

The implementation of epsilon ResNet

Robustdl_gan ⭐ 16

PyTorch implementation of NICE

Neuboots ⭐ 12

Neural Bootstrapper (NeuBoots)

Centeredwn ⭐ 11

This project is the Torch implementation of our ICCV 2017 paper: Centered Weight Normalization in Accelerating Training of Deep Neural Networks

Mobilenetv3 ⭐ 9

An implementation of MobileNetV3 with pyTorch

Semisupervised Learning With Dcgans ⭐ 9

Using DCGANs to learn useful representations during the adversarial training process and using the learned features to classify images with relatively little training data

Pytorch Quant ⭐ 9

DL quantization for pytorch

Entangled Watermark ⭐ 8

Pytorch Demo ⭐ 8

A small demo for training cnn with pytorch.

Ood Detection ⭐ 7

Code for "Zero-Shot Out-of-Distribution Detection with Feature Correlations"

Code for reproducing experiments in "SCGAN: Disentangled Representation Learning by Adding Similarity Constraint on Generative Adversarial Nets"

Semisup_scarse ⭐ 6

PyTorch implementation of Semi-Supervised Learning with Scarce Annotations https://arxiv.org/pdf/1905.08845.pdf

Wasserstein Bigan ⭐ 6

Wasserstein BiGAN (Bidirectional GAN trained using Wasserstein distance)

Tf_datasets ⭐ 5

Python scripts to download public datasets and generate tfrecords.

FALCON for compressing CNN

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