104 Open source projects that are alternatives of or similar to Awesome Papers Fewshot

Python module to interface with OpenML

A classified list of meta learning papers based on realm.

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

ProMP: Proximal Meta-Policy Search

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

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

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

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

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

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

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

My personal toolkit for PyTorch development.

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

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

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

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

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

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

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

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

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

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

A PyTorch Library for Meta-learning Research

Memory efficient MAML using gradient checkpointing

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

Meta learning with BERT as a learner

Graph meta learning via local subgraphs (NeurIPS 2020)

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

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

Tensorflow Implementation of MAML

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

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

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

Meta-Feature Extractor

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

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

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

Repository for few-shot learning machine learning projects

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.

Automated Machine Learning with scikit-learn

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

A dataset of datasets for learning to learn from few examples

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