104 Open source projects that are alternatives of or similar to Feat

Meta-Feature Extractor

[CVPR'17] Training a Correlation Filter end-to-end allows lightweight networks of 2 layers (600 kB) to high performance at fast speed..

Paper Lists, Notes and Slides, Focus on NLP. For summarization, please refer to https://github.com/xcfcode/Summarization-Papers

Tensorflow Implementation of MAML

Implementation of "Make One-Shot Video Object Segmentation Efficient Again” and the semi-supervised fine-tuning "e-OSVOS" approach (NeurIPS 2020).

PyTorch Implementations For A Series Of Deep Learning-Based Recommendation Models (IN PROGRESS)

PyTorch code for CVPR 2018 paper: Learning to Compare: Relation Network for Few-Shot Learning (Few-Shot Learning part)

NeurIPS'19: Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting (Pytorch implementation for noisy labels).

TensorFlow and PyTorch implementation of "Meta-Transfer Learning for Few-Shot Learning" (CVPR2019)

A collection of extensions and data-loaders for few-shot learning & meta-learning in PyTorch

Combine multiple MNIST digits to create datasets with 100/1000 classes for few-shot learning/meta-learning

DropClass and DropAdapt - repository for the paper accepted to Speaker Odyssey 2020

pytorch implementation of Optimization as a Model for Few-shot Learning

Few-shot binary text classification with Induction Networks and Word2Vec weights initialization

Meta-Transfer Learning for Zero-Shot Super-Resolution (CVPR, 2020)

A resource list for causality in statistics, data science and physics

The source codes of the paper "Improving Few-shot Text Classification via Pretrained Language Representations" and "When Low Resource NLP Meets Unsupervised Language Model: Meta-pretraining Then Meta-learning for Few-shot Text Classification".

A list of high-quality (newest) AutoML works and lightweight models including 1.) Neural Architecture Search, 2.) Lightweight Structures, 3.) Model Compression, Quantization and Acceleration, 4.) Hyperparameter Optimization, 5.) Automated Feature Engineering.

Python module to interface with OpenML

Collection for Few-shot Learning

Implementation of the paper "Adversarial Attacks on Graph Neural Networks via Meta Learning".

Awesome Multitask Learning Resources

Learning to Learn how to Learn: Self-Adaptive Visual Navigation using Meta-Learning (https://arxiv.org/abs/1812.00971)

Meta graph convolutional neural network-assisted resilient swarm communications

Memory efficient MAML using gradient checkpointing

Graph meta learning via local subgraphs (NeurIPS 2020)

[IJCAI 2021] Cross-Domain Few-Shot Classification via Adversarial Task Augmentation

My personal toolkit for PyTorch development.

Codes and datasets for AAAI-2021 paper "Learning to Pre-train Graph Neural Networks"

ProMP: Proximal Meta-Policy Search

"Learning to learn by gradient descent by gradient descent "by PyTorch -- a simple re-implementation.

Meta R-CNN : Towards General Solver for Instance-level Low-shot Learning

Code accompanying the ICML-2018 paper "Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace"

Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation (ICLR 2020 spotlight)

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

Bilevel Optimization Library in Python for Multi-Task and Meta Learning

Hierarchical Co-occurrence Network with Prototype Loss for Few-shot Learning (PyTorch)

A list of recent papers about Meta / few-shot learning methods applied in NLP areas.

Repository for few-shot learning machine learning projects

A modular toolbox for meta-learning research with a focus on speed and reproducibility.

Automated Machine Learning with scikit-learn

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

A dataset of datasets for learning to learn from few examples

The implementation of "Self-Supervised Generalisation with Meta Auxiliary Learning" [NeurIPS 2019].

Reinforcement Learning Tutorial with Demo: DP (Policy and Value Iteration), Monte Carlo, TD Learning (SARSA, QLearning), Function Approximation, Policy Gradient, DQN, Imitation, Meta Learning, Papers, Courses, etc..

All materials you need for Federated Learning: blogs, videos, papers, and softwares, etc.

Meta-Learning with Differentiable Convex Optimization (CVPR 2019 Oral)

[ICLR'19] Meta-learning with differentiable closed-form solvers

This repo provides pytorch code which replicates the results of the Matching Networks for One Shot Learning paper on the Omniglot and MiniImageNet dataset

A classified list of meta learning papers based on realm.

Auto-tune the Entropy Temperature of Soft Actor-Critic via Metagradient - 7th ICML AutoML workshop 2020

A PyTorch Library for Meta-learning Research

Meta Self-learning for Multi-Source Domain Adaptation： A Benchmark

The code for paper "CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning"

Code for the Neural Processes website and replication of 4 papers on NPs. Pytorch implementation.

Tools for generating mini-ImageNet dataset and processing batches

Code for the paper "Evolved Policy Gradients"

A hyperparameter optimization and data collection toolbox for convenient and fast prototyping of machine-learning models.

Learning Meta Face Recognition in Unseen Domains, CVPR, Oral, 2020

Meta learning with BERT as a learner