xwolfs

xwolfs

Geek Repo

Github PK Tool:Github PK Tool

xwolfs's repositories

pytorch-deeplab-xception

DeepLab v3+ model in PyTorch. Support different backbones.

Language:PythonLicense:MITStargazers:0Issues:0Issues:0

Meta-Learning-Papers

Meta Learning / Learning to Learn / One Shot Learning / Few Shot Learning

Stargazers:0Issues:0Issues:0

f-statistic-loss-nips-2018

Learning Deep Disentangled Embeddings with the F-Statistic Loss (NIPS 2018)

Language:PythonLicense:MITStargazers:0Issues:0Issues:0

Tensorflow-Audio-Classification

Audio classification with VGGish as feature extractor in TensorFlow

Language:PythonLicense:Apache-2.0Stargazers:0Issues:0Issues:0

Detectron

FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.

Language:PythonLicense:Apache-2.0Stargazers:0Issues:0Issues:0

xlearn

High Performance, Easy-to-use, and Scalable Machine Learning Package

Language:C++License:Apache-2.0Stargazers:0Issues:0Issues:0

state-of-the-art-result-for-machine-learning-problems

This repository provides state of the art (SoTA) results for all machine learning problems. We do our best to keep this repository up to date. If you do find a problem's SoTA result is out of date or missing, please raise this as an issue or submit Google form (with this information: research paper name, dataset, metric, source code and year). We will fix it immediately.

License:Apache-2.0Stargazers:0Issues:0Issues:0

tensorpack

A Neural Net Training Interface on TensorFlow

Language:PythonLicense:Apache-2.0Stargazers:0Issues:0Issues:0

Deformable-ConvNets

Deformable Convolutional Networks

Language:PythonLicense:Apache-2.0Stargazers:0Issues:0Issues:0

the-gan-zoo

A list of all named GANs!

Language:PythonLicense:MITStargazers:0Issues:0Issues:0

ganhacks

starter from "How to Train a GAN?" at NIPS2016

Stargazers:0Issues:0Issues:0

GAN

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

Language:PythonStargazers:0Issues:0Issues:0

tf_cnnvis

CNN visualization tool in TensorFlow

Language:PythonLicense:MITStargazers:0Issues:0Issues:0

improved_wgan_training

Code for reproducing experiments in "Improved Training of Wasserstein GANs"

Language:PythonLicense:MITStargazers:0Issues:0Issues:0

python-machine-learning-book

The "Python Machine Learning" book code repository and info resource

Language:Jupyter NotebookLicense:MITStargazers:0Issues:0Issues:0

awesome-matlab

A curated list of awesome Matlab frameworks, libraries and software.

Stargazers:0Issues:0Issues:0
Stargazers:0Issues:0Issues:0

awesome-semantic-segmentation

awesome-semantic-segmentation

Stargazers:0Issues:0Issues:0
Language:C++Stargazers:5Issues:0Issues:0

models

Models built with TensorFlow

Language:PythonLicense:Apache-2.0Stargazers:0Issues:0Issues:0

TensorFlow-Examples

TensorFlow Tutorial and Examples for beginners

Language:Jupyter NotebookLicense:NOASSERTIONStargazers:0Issues:0Issues:0

Tensorflow-GAN

GAN / DCGAN / InfoGAN / BEGAN ...

Language:Jupyter NotebookStargazers:0Issues:0Issues:0

inat_comp

iNaturalist competition details

Language:PythonLicense:MITStargazers:0Issues:0Issues:0

Convolutional-LSTM-in-Tensorflow

An implementation of convolutional lstms in tensorflow. The code is written in the same style as the basiclstmcell function in tensorflow

Language:PythonLicense:Apache-2.0Stargazers:0Issues:0Issues:0

unsupervised-video

[CVPR 2017] Unsupervised deep learning using unlabelled videos on the web

Language:ShellLicense:MITStargazers:0Issues:0Issues:0

tensorflow-zh

谷歌全新开源人工智能系统TensorFlow官方文档中文版

Language:TeXStargazers:0Issues:0Issues:0

AdversarialNetsPapers

The classical papers and codes about generative adversarial nets

Stargazers:0Issues:0Issues:0

awesome-deep-learning-papers

The most cited deep learning papers

Stargazers:0Issues:0Issues:0

keras

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on Theano or TensorFlow.

Language:PythonLicense:NOASSERTIONStargazers:0Issues:0Issues:0

mriqc

The package provides a series of image processing workflows to extract and compute a series of NR (no-reference), IQMs (image quality metrics) to be used in QAPs (quality assessment protocols) for MRI (magnetic resonance imaging).

Language:Jupyter NotebookLicense:BSD-3-ClauseStargazers:0Issues:0Issues:0