domain-transfer-network - TensorFlow Implementation of "Unsupervised Cross-Domain Image Generation"

  •        14

TensorFlow implementation of Unsupervised Cross-Domain Image Generation.

https://github.com/yunjey/domain-transfer-network

Tags
Implementation
License
Platform

   




Related Projects

awesome-transfer-learning - Best transfer learning and domain adaptation resources (papers, tutorials, datasets, etc

  •    

A list of awesome papers and cool resources on transfer learning, domain adaptation and domain-to-domain translation in general! As you will notice, this list is currently mostly focused on domain adaptation (DA), but don't hesitate to suggest resources in other subfields of transfer learning. I accept pull requests. Papers are ordered by theme and inside each theme by publication date (submission date for arXiv papers). If the network or algorithm is given a name in a paper, this one is written in bold before the paper's name.

Xvision - Chest Xray image analysis using Deep learning !

  •    Python

Chest Xray image analysis using Deep Learning and exploiting Deep Transfer Learning technique for it with Tensorflow. The maxpool-5 layer of a pretrained VGGNet-16(Deep Convolutional Neural Network) model has been used as the feature extractor here and then further trained on a 2-layer Deep neural network with SGD optimizer and Batch Normalization for classification of Normal vs Nodular Chest Xray Images.

tensorflow-image-detection - A generic image detection program that uses Google's Machine Learning library, Tensorflow and a pre-trained Deep Learning Convolutional Neural Network model called Inception

  •    Python

A generic image detection program that uses Google's Machine Learning library, Tensorflow and a pre-trained Deep Learning Convolutional Neural Network model called Inception. This model has been pre-trained for the ImageNet Large Visual Recognition Challenge using the data from 2012, and it can differentiate between 1,000 different classes, like Dalmatian, dishwasher etc. The program applies Transfer Learning to this existing model and re-trains it to classify a new set of images.

hub - A library for transfer learning by reusing parts of TensorFlow models.

  •    Python

TensorFlow Hub is a library to foster the publication, discovery, and consumption of reusable parts of machine learning models. In particular, it provides modules, which are pre-trained pieces of TensorFlow models that can be reused on new tasks. If you'd like to contribute to TensorFlow Hub, be sure to review the contribution guidelines. This project adheres to TensorFlow's code of conduct. By participating, you are expected to uphold this code.

DiscoGAN-pytorch - PyTorch implementation of "Learning to Discover Cross-Domain Relations with Generative Adversarial Networks"

  •    Jupyter

PyTorch implementation of Learning to Discover Cross-Domain Relations with Generative Adversarial Networks. * All samples in README.md are genearted by neural network except the first image for each row. * Network structure is slightly diffferent (here) from the author's code.


transferlearning - Everything about Transfer Learning and Domain Adaptation--迁移学习

  •    Python

Everything about Transfer Learning and Domain Adaptation--迁移学习

ladder - Ladder network is a deep learning algorithm that combines supervised and unsupervised learning

  •    Python

This is an implementation of Ladder Network in TensorFlow. Ladder network is a deep learning algorithm that combines supervised and unsupervised learning. It was introduced in the paper Semi-Supervised Learning with Ladder Network by A Rasmus, H Valpola, M Honkala, M Berglund, and T Raiko.

neural-style-tf - TensorFlow (Python API) implementation of Neural Style

  •    Python

Additionally, techniques are presented for semantic segmentation and multiple style transfer. The relative weight of the style and content can be controlled.

nginx-buildpack - Run NGINX in front of your app server on Heroku

  •    Shell

Nginx-buildpack vendors NGINX inside a dyno and connects NGINX to an app server via UNIX domain sockets. Some application servers (e.g. Ruby's Unicorn) halt progress when dealing with network I/O. Heroku's Cedar routing stack buffers only the headers of inbound requests. (The Cedar router will buffer the headers and body of a response up to 1MB) Thus, the Heroku router engages the dyno during the entire body transfer –from the client to dyno. For applications servers with blocking I/O, the latency per request will be degraded by the content transfer. By using NGINX in front of the application server, we can eliminate a great deal of transfer time from the application server. In addition to making request body transfers more efficient, all other I/O should be improved since the application server need only communicate with a UNIX socket on localhost. Basically, for webservers that are not designed for efficient, non-blocking I/O, we will benefit from having NGINX to handle all I/O operations.

transferlearning-tutorial - 《迁移学习简明手册》LaTex源码

  •    TeX

Jindong Wang et al. Transfer Learning Tutorial. 2018. 王晋东等. 迁移学习简明手册. 2018.

text-classification-models-tf - Tensorflow implementations of Text Classification Models.

  •    Python

Tensorflow implementation of Text Classification Models. Semi-supervised text classification(Transfer learning) models are implemented at [dongjun-Lee/transfer-learning-text-tf].

CycleGAN - Software that can generate photos from paintings, turn horses into zebras, perform style transfer, and more

  •    Lua

This package includes CycleGAN, pix2pix, as well as other methods like BiGAN/ALI and Apple's paper S+U learning. The code was written by Jun-Yan Zhu and Taesung Park. Note: Please check out PyTorch implementation for CycleGAN and pix2pix. The PyTorch version is under active development and can produce results comparable or better than this Torch version.

artistic-style-transfer - Convolutional neural networks for artistic style transfer.

  •    Jupyter

This repository contains (TensorFlow and Keras) code that goes along with a related blog post and talk (PDF). Together, they act as a systematic look at convolutional neural networks from theory to practice, using artistic style transfer as a motivating example. The blog post provides context and covers the underlying theory, while working through the Jupyter notebooks in this repository offers a more hands-on learning experience. If you have any questions about any of this stuff, feel free to open an issue or tweet at me: @copingbear.

Deep-Image-Analogy - The source code of 'Visual Attribute Transfer through Deep Image Analogy'.

  •    C++

The major contributors of this repository include Jing Liao, Yuan Yao, Lu Yuan, Gang Hua and Sing Bing Kang at Microsoft Research. Deep Image Analogy is a technique to find semantically-meaningful dense correspondences between two input images. It adapts the notion of image analogy with features extracted from a Deep Convolutional Neural Network.

All-About-the-GAN - All About the GANs(Generative Adversarial Networks) - Summarized lists for GAN

  •    Python

The purpose of this repository is providing the curated list of the state-of-the-art works on the field of Generative Adversarial Networks since their introduction in 2014. You can also check out the same data in a tabular format with functionality to filter by year or do a quick search by title here.

Citadel

  •    C

Citadel is a collaboration suite (messaging and groupware). It provides support for Email, Calendaring/Scheduling, Address books, Bulletin boards, Mailing List Server, Instant Messaging, Wiki, Multiple domain support.

simulated-unsupervised-tensorflow - TensorFlow implementation of "Learning from Simulated and Unsupervised Images through Adversarial Training"

  •    Python

TensorFlow implementation of Learning from Simulated and Unsupervised Images through Adversarial Training. Result of lambda=1.0 with optimizer=sgd after 8,000 steps.

rdb - Javascript ORM

  •    Javascript

ORM for nodejs. Supports postgres, mySql and sqlite. 1.7.1 Support for schemas (postgres only). 1.7.0 sqlite3 is now a peer dependency. Add it to your own package.json if you intend to use it. 1.6.9 Bugfix: one-to-many relation returns empty if strategy is included. 1.6.8 Bugfix: one-to-many relation returns empty if insert/update is done earlier in transaction. 1.6.7 Bugfix in relations. 1.6.6 Bugfix. 1.6.5 Improved performance on relations. 1.6.4 Bugfix. 1.6.3 Bugfix: potential incorrect timeZoneOffset when serializing date to JSON. Got timeZoneOffset from now() instead of on actual date. 1.6.2 Removed es6 syntax to ensure backwards compatability. Fixed global var leak. 1.6.1 Now supporting sqlite. 1.6.0 Bugfix: potential ambigous column error when using limit and relating to other tables. 1.5.9 Bugfix: using multipleStatements in mySql could sometimes cause an error when updates are run right before a select. Improved performance on limit when relating to other tables. Using uuid instead of node-uuid Updated all dependencies but generic-pool to latest. (Generic-pool has some breaking changes in latest. I will update it in next release.) 1.5.8 Cleanup line breaks in documentation. 1.5.7 Bugfix: getById.exclusive and tryGetById.exclusive did not lock if row was cached. Improved performance on tryGetFirst. 1.5.6 Raw sql filters can accept sql both as string and as function. E.g. var filter = {sql: function() {return 'foo > 1';}}. 1.5.5 Optional locks for getMany, tryGetFirst and tryGetById. Instead of calling getMany(params) just call getMany.exclusive(params). Same syntax goes for tryGetFirst and tryGetById. This will result in SELECT FOR UPDATE. Bugfix: bulk deletes now accepts raw sql filters too. 1.5.4 Transaction locks. Postgres only. 1.5.3 Upgraded to pg 6.0.3 1.5.2 Improved performance and reduced memory footprint. 1.5.1 Documented JSON column type. Bug fix: Insert and foreign key violation. 1.5.0 JSON column type. Postgres json type does not support rdb filters. 1.4.1 Empty filter would sometimes cause invalid filter. 1.4.0 Raw SQL query. 1.3.0 getMany() now supports limit and orderBy - same syntax as in streaming. 1.2.3 Bugfix: iEqual gave incorrect sql when parameterized. 1.2.2 Exlusive no longer returns a clone of table. It has changes current table to exclusive locking. 1.2.1 Bugfix: Exclusive row locks 1.2.0 Exclusive row locks 1.1.0 Now supporting streaming. Requires postgres or MySQL >=5.7.7 1.0.8 README fixup. 1.0.7 Better performance on insert and update. 1.0.6 Bugfix: Transaction domain should not forward rdb singleton from old domain. 1.0.5 Documentation cleanup. 1.0.4 orderBy in toDto(). 1.0.3 toDto() using next tick on every thousandth row to avoid maximum call stack size exceeded. 1.0.2 Reduced number of simultaneous promises in order to avoid maximum call stack size exceeded. 1.0.1 Bugfix: Incorrect insert/updates on timestamp without timezone. The time was converted utc instead of stripping the timezone. 1.0.0 Transaction domain forwards properties from old domain. Semantic versioning from now on. 0.5.1 Improved performance 0.5.0 Logging: rdb.log(someFunc) logs sql and parameters. Raw sql filters. 0.4.9 New method: tryGetById. New filter: iEqual, postgres only. Bugfix: rows.toJSON() without strategy did not include any children. 0.4.8 Explicit pooling with size and end(). Bugfix: mySql did not release client to pool. 0.4.7 Upgraded to pg 4.3.0 Upgraded to mysql 2.5.5 0.4.6 Upgraded pg 4.2.0. 0.4.5 Oops. Forgot to use pg.js instead of pg. 0.4.4 Upgraded all dependencies to latest. Using pg.js instead of pg. 0.4.3 Can ignore columns when serializing to dto. 0.4.2 Bugfix: update on a row crashes when a delete occurs earlier in same transaction. 0.4.1 Bugfix: more global leaks. 0.4.0 Bugfix: global leak. 0.3.9 Bugfix: eager loading joins/hasOne with non unique column names was not handled correctly. 0.3.8 Supports mySql. Bulk deletes. 0.3.7 Bugfix: eager loading manyRelation on a join/hasOne returned empty array #11 0.3.6 Fixed sql injection vulnerability. 0.3.5 Built-in fetching strategies for lazy loading. Works best in readonly scenarios. 0.3.4 Docs and examples split moved to separate file. 0.3.3 Fixed documentation layout again. 0.3.2 Fixed documentation layout. 0.3.1 Case insensitive filters: iStartsWith, iEndsWith and iContains. 0.3.0 Fix broken links in docs. 0.2.9 Support for row.delete(). Rollback only throws when error is present. 0.2.8 Guid accepts uppercase letters. Bugfix: null inserts on guid columns yielded wrong sql. 0.2.7 New method, toDto(), converts row to data transfer object. Bugfix: toJSON returned incorrect string on hasMany relations. 0.2.6 Fixed incorrect links in README. 0.2.5 Bugfix: caching on composite keys could give a crash #7. Improved sql compression on insert/update. 0.2.4 Bugfix: getMany with many-strategy and shallowFilter yields incorrect query #6. 0.2.3 Reformatted documentation. No code changes.

glow - Compiler for Neural Network hardware accelerators

  •    C++

Glow is a machine learning compiler and execution engine for various hardware targets. It is designed to be used as a backend for high-level machine learning frameworks. The compiler is designed to allow state of the art compiler optimizations and code generation of neural network graphs. This library is experimental and in active development. Glow lowers a traditional neural network dataflow graph into a two-phase strongly-typed intermediate representation (IR). The high-level IR allows the optimizer to perform domain-specific optimizations. The lower-level instruction-based address-only IR allows the compiler to perform memory-related optimizations, such as instruction scheduling, static memory allocation and copy elimination. At the lowest level, the optimizer performs machine-specific code generation to take advantage of specialized hardware features. Glow features a lowering phase which enables the compiler to support a high number of input operators as well as a large number of hardware targets by eliminating the need to implement all operators on all targets. The lowering phase is designed to reduce the input space and allow new hardware backends to focus on a small number of linear algebra primitives. The design philosophy is described in an arXiv paper.

srgan - Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

  •    Python

We run this script under TensorFlow 1.4 and the TensorLayer 1.8.0+. 🚀 This repo will be moved to here (please star) for life-cycle management soon. More cool Computer Vision applications such as pose estimation and style transfer can be found in this organization.