mliisCode for meta-learning initializations for image segmentation
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MetaBIN[CVPR2021] Meta Batch-Instance Normalization for Generalizable Person Re-Identification
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meta-interpolationSource code for CVPR 2020 paper "Scene-Adaptive Video Frame Interpolation via Meta-Learning"
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sib meta learnCode of Empirical Bayes Transductive Meta-Learning with Synthetic Gradients
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FSL-MateFSL-Mate: A collection of resources for few-shot learning (FSL).
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Awesome Automl And Lightweight ModelsA 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.
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StyleSpeechOfficial implementation of Meta-StyleSpeech and StyleSpeech
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PAMLPersonalizing Dialogue Agents via Meta-Learning
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maml-tensorflowThis repository implements the paper, Model-Agnostic Meta-Leanring for Fast Adaptation of Deep Networks.
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NSLImplementation for <Neural Similarity Learning> in NeurIPS'19.
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Meta Transfer LearningTensorFlow and PyTorch implementation of "Meta-Transfer Learning for Few-Shot Learning" (CVPR2019)
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Learningtocompare fslPyTorch code for CVPR 2018 paper: Learning to Compare: Relation Network for Few-Shot Learning (Few-Shot Learning part)
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MetaHeacThis is an official implementation for "Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and Advertising"(KDD2021).
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e-osvosImplementation of "Make One-Shot Video Object Segmentation Efficient Again” and the semi-supervised fine-tuning "e-OSVOS" approach (NeurIPS 2020).
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mindwareAn efficient open-source AutoML system for automating machine learning lifecycle, including feature engineering, neural architecture search, and hyper-parameter tuning.
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MfeMeta-Feature Extractor
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meta-learning-progressRepository to track the progress in Meta-Learning (MtL), including the datasets and the current state-of-the-art for the most common MtL problems.
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dropclass speakerDropClass and DropAdapt - repository for the paper accepted to Speaker Odyssey 2020
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Cfnet[CVPR'17] Training a Correlation Filter end-to-end allows lightweight networks of 2 layers (600 kB) to high performance at fast speed..
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simple-cnapsSource 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)
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Open-L2OOpen-L2O: A Comprehensive and Reproducible Benchmark for Learning to Optimize Algorithms
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Meta-TTSOfficial repository of https://arxiv.org/abs/2111.04040v1
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FOCAL-ICLRCode for FOCAL Paper Published at ICLR 2021
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Reinforcement learning tutorial with demoReinforcement 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..
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Hcn Prototypeloss PytorchHierarchical Co-occurrence Network with Prototype Loss for Few-shot Learning (PyTorch)
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MetaLifelongLanguageRepository containing code for the paper "Meta-Learning with Sparse Experience Replay for Lifelong Language Learning".
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MetaoptnetMeta-Learning with Differentiable Convex Optimization (CVPR 2019 Oral)
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metagenrlMetaGenRL, a novel meta reinforcement learning algorithm. Unlike prior work, MetaGenRL can generalize to new environments that are entirely different from those used for meta-training.
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Mt NetCode accompanying the ICML-2018 paper "Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace"
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Meta-DETRMeta-DETR: Official PyTorch Implementation
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MatchingnetworksThis repo provides pytorch code which replicates the results of the Matching Networks for One Shot Learning paper on the Omniglot and MiniImageNet dataset
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meta-SRPytorch implementation of Meta-Learning for Short Utterance Speaker Recognition with Imbalance Length Pairs (Interspeech, 2020)
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Few ShotRepository for few-shot learning machine learning projects
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LearningToCompare-TensorflowTensorflow implementation for paper: Learning to Compare: Relation Network for Few-Shot Learning.
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Meta-SACAuto-tune the Entropy Temperature of Soft Actor-Critic via Metagradient - 7th ICML AutoML workshop 2020
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HebbianMetaLearningMeta-Learning through Hebbian Plasticity in Random Networks: https://arxiv.org/abs/2007.02686
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Learning To Learn By Pytorch"Learning to learn by gradient descent by gradient descent "by PyTorch -- a simple re-implementation.
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pymfePython Meta-Feature Extractor package.
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Meta-SelfLearningMeta Self-learning for Multi-Source Domain Adaptation: A Benchmark
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Nearest-Celebrity-FaceTensorflow Implementation of FaceNet: A Unified Embedding for Face Recognition and Clustering to find the celebrity whose face matches the closest to yours.
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Auto SklearnAutomated Machine Learning with scikit-learn
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SAN[ECCV 2020] Scale Adaptive Network: Learning to Learn Parameterized Classification Networks for Scalable Input Images
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CDFSL-ATA[IJCAI 2021] Cross-Domain Few-Shot Classification via Adversarial Task Augmentation
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MetaGymCollection of Reinforcement Learning / Meta Reinforcement Learning Environments.
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TransferlearningTransfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习
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FUSIONPyTorch code for NeurIPSW 2020 paper (4th Workshop on Meta-Learning) "Few-Shot Unsupervised Continual Learning through Meta-Examples"
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MLSRSource code for ECCV2020 "Fast Adaptation to Super-Resolution Networks via Meta-Learning"
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Meta DatasetA dataset of datasets for learning to learn from few examples
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maml-rl-tf2Implementation of Model-Agnostic Meta-Learning (MAML) applied on Reinforcement Learning problems in TensorFlow 2.
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Maml TfTensorflow Implementation of MAML
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Few Shot Text ClassificationFew-shot binary text classification with Induction Networks and Word2Vec weights initialization
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LooperA resource list for causality in statistics, data science and physics
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MeTALOfficial PyTorch implementation of "Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning" (ICCV2021 Oral)
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