146 Open source projects that are alternatives of or similar to sib_meta_learn

Source codes for "Improved Few-Shot Visual Classification" (CVPR 2020), "Enhancing Few-Shot Image Classification with Unlabelled Examples" (WACV 2022), and "Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning" (Neural Networks 2022 - in submission)

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

FSL-Mate: A collection of resources for few-shot learning (FSL).

Official repository of https://arxiv.org/abs/2111.04040v1

Awesome Few-shot learning

Tensorflow implementation for paper: Learning to Compare: Relation Network for Few-Shot Learning.

awesome few shot / meta learning papers

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

LibFewShot: A Comprehensive Library for Few-shot Learning.

Learning To Compare For Text , Few shot learning in text classification

Official PyTorch implementation of "Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning" (ICCV2021 Oral)

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

PyTorch code for NeurIPSW 2020 paper (4th Workshop on Meta-Learning) "Few-Shot Unsupervised Continual Learning through Meta-Examples"

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

Knowledge-Aware machine LEarning (KALE): accessible machine learning from multiple sources for interdisciplinary research, part of the 🔥PyTorch ecosystem

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

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

Code for FOCAL Paper Published at ICLR 2021

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)

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

📄 Spatial Contrastive Learning for Few-Shot Classification (ECML/PKDD 2021).

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

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

Dialogue Knowledge Transfer Networks (DiKTNet)

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

finetune bert for small dataset text classification in a few-shot learning manner using ProtoNet

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 classified list of meta learning papers based on realm.

ProMP: Proximal Meta-Policy Search

[CVPR 2021] Few-shot 3D Point Cloud Semantic Segmentation

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

TensorFlow 2.0 implementation of MAML.

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

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

Code for ACL'19 "Few-Shot Representation Learning for Out-Of-Vocabulary Words"

My personal toolkit for PyTorch development.

Great resources for making Reinforcement Learning work in Real Life situations. Papers,projects and more.

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

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

Implementation of our paper "Meta Reinforcement Learning with Task Embedding and Shared Policy"

Sinkhorn Label Allocation is a label assignment method for semi-supervised self-training algorithms. The SLA algorithm is described in full in this ICML 2021 paper: https://arxiv.org/abs/2102.08622.

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

Code for "One-Shot Visual Imitation Learning via Meta-Learning"

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

Few-shot Keyword Spotting in Any Language and Multilingual Spoken Word Corpus

The code repository for "Few-Shot Learning via Embedding Adaptation with Set-to-Set Functions"

[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

Source code for ECCV2020 "Fast Adaptation to Super-Resolution Networks via Meta-Learning"

A novel method to tune language models. Codes and datasets for paper ``GPT understands, too''.

Tools for generating mini-ImageNet dataset and processing batches

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)

PyTorch implementation of 'Squeeze and Excite' Guided Few Shot Segmentation of Volumetric Scans

Code for the paper "Evolved Policy Gradients"

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