renet[ICCV'21] Official PyTorch implementation of Relational Embedding for Few-Shot Classification
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FSL-MateFSL-Mate: A collection of resources for few-shot learning (FSL).
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WARPCode for ACL'2021 paper WARP 🌀 Word-level Adversarial ReProgramming. Outperforming `GPT-3` on SuperGLUE Few-Shot text classification. https://aclanthology.org/2021.acl-long.381/
Stars: ✭ 66 (+22.22%)
LearningToCompare-TensorflowTensorflow implementation for paper: Learning to Compare: Relation Network for Few-Shot Learning.
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Black-Box-TuningICML'2022: Black-Box Tuning for Language-Model-as-a-Service
Stars: ✭ 99 (+83.33%)
brunoa deep recurrent model for exchangeable data
Stars: ✭ 34 (-37.04%)
pytorch-meta-datasetA non-official 100% PyTorch implementation of META-DATASET benchmark for few-shot classification
Stars: ✭ 39 (-27.78%)
ganbertEnhancing the BERT training with Semi-supervised Generative Adversarial Networks
Stars: ✭ 205 (+279.63%)
Few-NERDCode and data of ACL 2021 paper "Few-NERD: A Few-shot Named Entity Recognition Dataset"
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MLMANACL 2019 paper:Multi-Level Matching and Aggregation Network for Few-Shot Relation Classification
Stars: ✭ 59 (+9.26%)
LaplacianShotLaplacian Regularized Few Shot Learning
Stars: ✭ 72 (+33.33%)
FewShotDetection(ECCV 2020) PyTorch implementation of paper "Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wild"
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sib meta learnCode of Empirical Bayes Transductive Meta-Learning with Synthetic Gradients
Stars: ✭ 56 (+3.7%)
sinkhorn-label-allocationSinkhorn 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.
Stars: ✭ 49 (-9.26%)
FUSIONPyTorch code for NeurIPSW 2020 paper (4th Workshop on Meta-Learning) "Few-Shot Unsupervised Continual Learning through Meta-Examples"
Stars: ✭ 18 (-66.67%)
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)
Stars: ✭ 88 (+62.96%)
attMPTI[CVPR 2021] Few-shot 3D Point Cloud Semantic Segmentation
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multilingual kwsFew-shot Keyword Spotting in Any Language and Multilingual Spoken Word Corpus
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P-tuningA novel method to tune language models. Codes and datasets for paper ``GPT understands, too''.
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few-shot-segmentationPyTorch implementation of 'Squeeze and Excite' Guided Few Shot Segmentation of Volumetric Scans
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LibFewShotLibFewShot: A Comprehensive Library for Few-shot Learning.
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HiCECode for ACL'19 "Few-Shot Representation Learning for Out-Of-Vocabulary Words"
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SCL📄 Spatial Contrastive Learning for Few-Shot Classification (ECML/PKDD 2021).
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Meta Learning PapersMeta Learning / Learning to Learn / One Shot Learning / Few Shot Learning
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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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few-shot-gan-adaptation[CVPR '21] Official repository for Few-shot Image Generation via Cross-domain Correspondence
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adaptAwesome Domain Adaptation Python Toolbox
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FewCLUEFewCLUE 小样本学习测评基准,中文版
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few-shot-lmThe source code of "Language Models are Few-shot Multilingual Learners" (MRL @ EMNLP 2021)
Stars: ✭ 32 (-40.74%)
MemoPainter-PyTorchAn unofficial implementation of MemoPainter(Coloring With Limited Data: Few-shot Colorization via Memory Augmented Networks) using PyTorch framework.
Stars: ✭ 63 (+16.67%)
mmfewshotOpenMMLab FewShot Learning Toolbox and Benchmark
Stars: ✭ 336 (+522.22%)
CDFSL-ATA[IJCAI 2021] Cross-Domain Few-Shot Classification via Adversarial Task Augmentation
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Meta-Fine-Tuning[CVPR 2020 VL3] The repository for meta fine-tuning in cross-domain few-shot learning.
Stars: ✭ 29 (-46.3%)
MeTALOfficial PyTorch implementation of "Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning" (ICCV2021 Oral)
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finetunerFinetuning any DNN for better embedding on neural search tasks
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Meta-TTSOfficial repository of https://arxiv.org/abs/2111.04040v1
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lowshot-shapebiasLearning low-shot object classification with explicit shape bias learned from point clouds
Stars: ✭ 37 (-31.48%)
FRN(CVPR 2021) Few-Shot Classification with Feature Map Reconstruction Networks
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few shot slot tagging and NERPyTorch implementation of the paper: Vector Projection Network for Few-shot Slot Tagging in Natural Language Understanding. Su Zhu, Ruisheng Cao, Lu Chen and Kai Yu.
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Awesome-Weak-Shot-LearningA curated list of papers, code and resources pertaining to weak-shot classification, detection, and segmentation.
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deviation-networkSource code of the KDD19 paper "Deep anomaly detection with deviation networks", weakly/partially supervised anomaly detection, few-shot anomaly detection
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Meta-GDN AnomalyDetectionImplementation of TheWebConf 2021 -- Few-shot Network Anomaly Detection via Cross-network Meta-learning
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