Displaying 1 to 20 from 44 results

opencog - A framework for integrated Artificial Intelligence & Artificial General Intelligence (AGI)

  •    Scheme

OpenCog is a framework for developing AI systems, especially appropriate for integrative multi-algorithm systems, and artificial general intelligence systems. Though much work remains to be done, it currently contains a functional core framework, and a number of cognitive agents at varying levels of completion, some already displaying interesting and useful functionalities alone and in combination. With the exception of MOSES and the CogServer, all of the above are in active development, are half-baked, poorly documented, mis-designed, subject to experimentation, and generally in need of love an attention. This is where experimentation and integration are taking place, and, like any laboratory, things are a bit fluid and chaotic.

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.

SfMLearner - An unsupervised learning framework for depth and ego-motion estimation from monocular videos

  •    Jupyter

In CVPR 2017 (Oral). See the project webpage for more details. Please contact Tinghui Zhou (tinghuiz@berkeley.edu) if you have any questions.

OpenSelfSup - Self-Supervised Learning Toolbox and Benchmark

  •    Python

The master branch works with PyTorch 1.1 or higher. OpenSelfSup is an open source unsupervised representation learning toolbox based on PyTorch.

simclr - SimCLRv2 - Big Self-Supervised Models are Strong Semi-Supervised Learners

  •    Jupyter

News! We have released a TF2 implementation of SimCLR (along with converted checkpoints in TF2), they are in tf2/ folder. News! Colabs for Intriguing Properties of Contrastive Losses are added, see here.

alibi-detect - Algorithms for outlier, adversarial and drift detection

  •    Python

Alibi Detect is an open source Python library focused on outlier, adversarial and drift detection. The package aims to cover both online and offline detectors for tabular data, text, images and time series. Both TensorFlow and PyTorch backends are supported for drift detection. For more background on the importance of monitoring outliers and distributions in a production setting, check out this talk from the Challenges in Deploying and Monitoring Machine Learning Systems ICML 2020 workshop, based on the paper Monitoring and explainability of models in production and referencing Alibi Detect.

context-encoder - [CVPR 2016] Unsupervised Feature Learning by Image Inpainting using GANs

  •    Lua

If you could successfully run the above demo, run following steps to train your own context encoder model for image inpainting. Features for context encoder trained with reconstruction loss.

Pyod - A Python Toolkit for Scalable Outlier Detection (Anomaly Detection)

  •    Python

Important Notes: PyOD contains some neural network based models, e.g., AutoEncoders, which are implemented in keras. However, PyOD would NOT install keras and tensorflow automatically. This would reduce the risk of damaging your local installations. You are responsible for installing keras and tensorflow if you want to use neural net based models. An instruction is provided here. Anomaly detection resources, e.g., courses, books, papers and videos.

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.

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.

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.

OpenUnReID - PyTorch open-source toolbox for unsupervised or domain adaptive object re-ID.

  •    Python

OpenUnReID is an open-source PyTorch-based codebase for both unsupervised learning (USL) and unsupervised domain adaptation (UDA) in the context of object re-ID tasks. It provides strong baselines and multiple state-of-the-art methods with highly refactored codes for both pseudo-label-based and domain-translation-based frameworks. It works with Python >=3.5 and PyTorch >=1.1. We are actively updating this repo, and more methods will be supported soon. Contributions are welcome.

esapp - An unsupervised Chinese word segmentation tool.

  •    C++

See test_package/example.cpp. The recommended way to use ESA++ package in your project is to install the package with Conan.

sutton-barto-rl-exercises - Learning reinforcement learning by implementing the algorithms from reinforcement learning an introduction

  •    Jupyter

Pull requests and bug report are welcome. Note: if you find the formatting some notebooks (esp. with many equations) doesn't look good on github,, try visualize them on http://nbviewer.jupyter.org/github/zyxue/sutton-barto-rl-exercises/tree/master/.

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