sib meta learnCode of Empirical Bayes Transductive Meta-Learning with Synthetic Gradients
Stars: ✭ 56 (-70.21%)
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 (-73.94%)
FUSIONPyTorch code for NeurIPSW 2020 paper (4th Workshop on Meta-Learning) "Few-Shot Unsupervised Continual Learning through Meta-Examples"
Stars: ✭ 18 (-90.43%)
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 (-53.19%)
attMPTI[CVPR 2021] Few-shot 3D Point Cloud Semantic Segmentation
Stars: ✭ 118 (-37.23%)
multilingual kwsFew-shot Keyword Spotting in Any Language and Multilingual Spoken Word Corpus
Stars: ✭ 122 (-35.11%)
P-tuningA novel method to tune language models. Codes and datasets for paper ``GPT understands, too''.
Stars: ✭ 593 (+215.43%)
few-shot-segmentationPyTorch implementation of 'Squeeze and Excite' Guided Few Shot Segmentation of Volumetric Scans
Stars: ✭ 78 (-58.51%)
LibFewShotLibFewShot: A Comprehensive Library for Few-shot Learning.
Stars: ✭ 629 (+234.57%)
HiCECode for ACL'19 "Few-Shot Representation Learning for Out-Of-Vocabulary Words"
Stars: ✭ 56 (-70.21%)
SCL📄 Spatial Contrastive Learning for Few-Shot Classification (ECML/PKDD 2021).
Stars: ✭ 42 (-77.66%)
Meta Learning PapersMeta Learning / Learning to Learn / One Shot Learning / Few Shot Learning
Stars: ✭ 2,420 (+1187.23%)
TransferlearningTransfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习
Stars: ✭ 8,481 (+4411.17%)
few-shot-gan-adaptation[CVPR '21] Official repository for Few-shot Image Generation via Cross-domain Correspondence
Stars: ✭ 198 (+5.32%)
adaptAwesome Domain Adaptation Python Toolbox
Stars: ✭ 46 (-75.53%)
FewCLUEFewCLUE 小样本学习测评基准,中文版
Stars: ✭ 251 (+33.51%)
few-shot-lmThe source code of "Language Models are Few-shot Multilingual Learners" (MRL @ EMNLP 2021)
Stars: ✭ 32 (-82.98%)
MemoPainter-PyTorchAn unofficial implementation of MemoPainter(Coloring With Limited Data: Few-shot Colorization via Memory Augmented Networks) using PyTorch framework.
Stars: ✭ 63 (-66.49%)
mmfewshotOpenMMLab FewShot Learning Toolbox and Benchmark
Stars: ✭ 336 (+78.72%)
CDFSL-ATA[IJCAI 2021] Cross-Domain Few-Shot Classification via Adversarial Task Augmentation
Stars: ✭ 21 (-88.83%)
Meta-Fine-Tuning[CVPR 2020 VL3] The repository for meta fine-tuning in cross-domain few-shot learning.
Stars: ✭ 29 (-84.57%)
MeTALOfficial PyTorch implementation of "Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning" (ICCV2021 Oral)
Stars: ✭ 24 (-87.23%)
finetunerFinetuning any DNN for better embedding on neural search tasks
Stars: ✭ 442 (+135.11%)
Meta-TTSOfficial repository of https://arxiv.org/abs/2111.04040v1
Stars: ✭ 69 (-63.3%)
lowshot-shapebiasLearning low-shot object classification with explicit shape bias learned from point clouds
Stars: ✭ 37 (-80.32%)
FRN(CVPR 2021) Few-Shot Classification with Feature Map Reconstruction Networks
Stars: ✭ 43 (-77.13%)
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.
Stars: ✭ 17 (-90.96%)
Awesome-Weak-Shot-LearningA curated list of papers, code and resources pertaining to weak-shot classification, detection, and segmentation.
Stars: ✭ 142 (-24.47%)
deviation-networkSource code of the KDD19 paper "Deep anomaly detection with deviation networks", weakly/partially supervised anomaly detection, few-shot anomaly detection
Stars: ✭ 94 (-50%)
Meta-GDN AnomalyDetectionImplementation of TheWebConf 2021 -- Few-shot Network Anomaly Detection via Cross-network Meta-learning
Stars: ✭ 22 (-88.3%)
matching-networksMatching Networks for one-shot learning in tensorflow (NIPS'16)
Stars: ✭ 54 (-71.28%)
renet[ICCV'21] Official PyTorch implementation of Relational Embedding for Few-Shot Classification
Stars: ✭ 72 (-61.7%)
FSL-MateFSL-Mate: A collection of resources for few-shot learning (FSL).
Stars: ✭ 1,346 (+615.96%)
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 (-64.89%)
LearningToCompare-TensorflowTensorflow implementation for paper: Learning to Compare: Relation Network for Few-Shot Learning.
Stars: ✭ 17 (-90.96%)
Black-Box-TuningICML'2022: Black-Box Tuning for Language-Model-as-a-Service
Stars: ✭ 99 (-47.34%)
brunoa deep recurrent model for exchangeable data
Stars: ✭ 34 (-81.91%)
pytorch-meta-datasetA non-official 100% PyTorch implementation of META-DATASET benchmark for few-shot classification
Stars: ✭ 39 (-79.26%)
ganbertEnhancing the BERT training with Semi-supervised Generative Adversarial Networks
Stars: ✭ 205 (+9.04%)
Few-NERDCode and data of ACL 2021 paper "Few-NERD: A Few-shot Named Entity Recognition Dataset"
Stars: ✭ 317 (+68.62%)
MLMANACL 2019 paper:Multi-Level Matching and Aggregation Network for Few-Shot Relation Classification
Stars: ✭ 59 (-68.62%)
LaplacianShotLaplacian Regularized Few Shot Learning
Stars: ✭ 72 (-61.7%)
Meta-DETRMeta-DETR: Official PyTorch Implementation
Stars: ✭ 205 (+9.04%)
DCNetDense Relation Distillation with Context-aware Aggregation for Few-Shot Object Detection, CVPR 2021
Stars: ✭ 113 (-39.89%)