283 Open source projects that are alternatives of or similar to game-feature-learning

VisDA2020: 4th Visual Domain Adaptation Challenge in ECCV'20

Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习

Repository to track the progress in Meta-Learning (MtL), including the datasets and the current state-of-the-art for the most common MtL problems.

Official repository for the ShapeFormer Project

An implementation of "Community Preserving Network Embedding" (AAAI 2017)

1st to MICCAI DigestPath2019 challenge (https://digestpath2019.grand-challenge.org/Home/) on colonoscopy tissue segmentation and classification task. (MICCAI 2019) https://teacher.bupt.edu.cn/zhuchuang/en/index.htm

A PyTorch implementation for Unsupervised Domain Adaptation by Backpropagation

CrossNER: Evaluating Cross-Domain Named Entity Recognition (AAAI-2021)

Implementation of Information Dropout

code to train a neural network to align pairs of shapes without needing ground truth warps for supervision

Code for NAACL 2019 paper: Adversarial Category Alignment Network for Cross-domain Sentiment Classification

Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes, ICCV 2017

FixBi: Bridging Domain Spaces for Unsupervised Domain Adaptation (CVPR 2021)

Speckle2Void: Deep Self-Supervised SAR Despeckling with Blind-Spot Convolutional Neural Networks

pair2vec: Compositional Word-Pair Embeddings for Cross-Sentence Inference

ExCon: Explanation-driven Supervised Contrastive Learning

This repository contains the code implementation used in the paper Temporally Coherent Embeddings for Self-Supervised Video Representation Learning (TCE).

Evaluate real and synthetic datasets with each other

An attempt at the implementation of GLOM, Geoffrey Hinton's paper for emergent part-whole hierarchies from data

This repository contains the collection of UCI (real-life) datasets and Synthetic (artificial) datasets (with cluster labels and MATLAB files) ready to use with clustering algorithms.

Representation learning-based graph alignment based on implicit matrix factorization and structural embeddings

Reference github repository for the paper "Improving Single-Image Defocus Deblurring: How Dual-Pixel Images Help Through Multi-Task Learning". We propose a single-image deblurring network that incorporates the two sub-aperture views into a multitask framework. Specifically, we show that jointly learning to predict the two DP views from a single …

What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment [CVPR 2019]

NLU: domain-intent-slot; text2SQL

code for our ECCV 2020 paper "A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation"

Pytorch implementation of Feature Generation for Long-Tail Classification by Rahul Vigneswaran, Marc T Law, Vineeth N Balasubramaniam and Makarand Tapaswi

Must-read papers and resources related to causal inference and machine (deep) learning

A collection of tasks to probe the effectiveness of protein sequence representations in modeling aspects of protein design

Source code for GNN-LSPE (Graph Neural Networks with Learnable Structural and Positional Representations), ICLR 2022

Tutorials for Machine Learning on Graphs

Proto-RL: Reinforcement Learning with Prototypical Representations

Synthetic Data Generation for mixed-type, multivariate time series.

Unsupervised Representation Learning for Singing Voice Separation

Official adversarial mixup resynthesis repository

Automatic feature engineering using deep learning and Bayesian inference using TensorFlow.

Parametric UMAP embeddings for representation and semisupervised learning. From the paper "Parametric UMAP: learning embeddings with deep neural networks for representation and semi-supervised learning" (Sainburg, McInnes, Gentner, 2020).

Benchmarking synthetic data generation methods.

A complete end-to-end demonstration in which we collect training data in Unity and use that data to train a deep neural network to predict the pose of a cube. This model is then deployed in a simulated robotic pick-and-place task.

Awesome Graph Self-Supervised Learning

Official Detectron2 implementation of DA-RetinaNet of our Image and Vision Computing 2021 work 'An unsupervised domain adaptation scheme for single-stage artwork recognition in cultural sites'

A library for evaluating representations.

Three-Filters-to-Normal: An Accurate and Ultrafast Surface Normal Estimator (RAL+ICRA'21)

The Gretel Python Client allows you to interact with the Gretel REST API.

Code for Paper ''Dual Student: Breaking the Limits of the Teacher in Semi-Supervised Learning'' [ICCV 2019]

The Declarative Data Generator

Official code for "Mean Shift for Self-Supervised Learning"

Transferable End-to-End Aspect-based Sentiment Analysis with Selective Adversarial Learning (EMNLP'19)

Unofficial pytorch implementation of algorithms for domain adaptation

[AAAI 2020] Code release for "Domain Conditioned Adaptation Network" https://arxiv.org/abs/2005.06717

Simple and extensible GAN image-to-image translation framework. Supports natural and medical images.

Adapt Transformer-based language models to new text domains

Meta-embeddings are a probabilistic generalization of embeddings in machine learning.

Code and datasets for EMNLP2018 paper ‘‘Adaptive Semi-supervised Learning for Cross-domain Sentiment Classification’’.

Simplified version of "State Representation Learning for Control: An Overview" bibliography

[ICCV 2021] Excavating the Potential Capacity of Self-Supervised Monocular Depth Estimation

Synthetic data for computer vision. An open source toolkit using Blender and Python.

Implementation of many transfer learning algorithms in Python with Jupyter notebooks

[ICML 2021] Break-It-Fix-It: Unsupervised Learning for Program Repair

Pytorch implementation of "A Simple Framework for Contrastive Learning of Visual Representations"

The reference implementation of FEATHER from the CIKM '20 paper "Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models".