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adversarial-attacks-pytorch

A pytorch implementations of Adversarial attacks and utils

alfred

alfred-py: A deep learning utility library for visualization and sensor fusion purpose

attention_branch_network

Attention Branch Network (CIFAR100, ImageNet models)

automold--road-augmentation-library

This library augments road images to introduce various real world scenarios that pose challenges for training neural networks of Autonomous vehicles. Automold is created to train CNNs in specific weather and road conditions.

balanced-dataparallel

这里是改进了pytorch的DataParallel, 用来平衡第一个GPU的显存使用量

covid-ct

COVID-CT-Dataset: A CT Scan Dataset about COVID-19

detecting-the-unexpected

Detecting the Unexpected via Image Resynthesis

detection-of-traffic-anomaly

This is the repo for our Detection of Traffic Anomaly (DoTA) dataset.

explanatorygraph

Interpreting CNN Knowledge via an Explanatory Graph

fast

fftw-example

Simple example demonstrating a frequency transform with FFTW

gan

Resources and Implementations of Generative Adversarial Nets: GAN, DCGAN, WGAN, CGAN, InfoGAN

hello-world

This my first updata. Just a test.

ia-cnn

interpretable-cnn-song

interpretablecnn

interpretablecnn-1

ml_rfi

Machine learning applied to RFI flagging

mnad

An official implementation of "Learning Memory-guided Normality for Anomaly Detection" (CVPR 2020) in PyTorch.

moderngpu

Patterns and behaviors for GPU computing

novelty-detection

Latent space autoregression for novelty detection.

perception

presto

Open source pulsar search and analysis toolkit

psrdada

Local changes for AA-Alert for the PRSDada libraray

pytorch-fgvc-dataset

PyTorch custom dataset APIs -- CUB-200-2011, Stanford Dogs, Stanford Cars, FGVC Aircraft, NABirds, Tiny ImageNet, iNaturalist2017

racnn-pytorch

This is a third party implementation of RA-CNN in pytorch.

rfi_monitor

DRAO RFI project

rfim

Radio Frequency Interference (RFI) mitigation algorithms.

seraproject

Software-Enabled Radio Astronomy

simple-faster-rcnn-pytorch

A simplified implemention of Faster R-CNN that replicate performance from origin paper