CDFSL-ATA[IJCAI 2021] Cross-Domain Few-Shot Classification via Adversarial Task Augmentation
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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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lowshot-shapebiasLearning low-shot object classification with explicit shape bias learned from point clouds
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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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matching-networksMatching Networks for one-shot learning in tensorflow (NIPS'16)
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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/
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Black-Box-TuningICML'2022: Black-Box Tuning for Language-Model-as-a-Service
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brunoa deep recurrent model for exchangeable data
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ganbertEnhancing the BERT training with Semi-supervised Generative Adversarial Networks
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MLMANACL 2019 paper:Multi-Level Matching and Aggregation Network for Few-Shot Relation Classification
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LaplacianShotLaplacian Regularized Few Shot Learning
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sib meta learnCode of Empirical Bayes Transductive Meta-Learning with Synthetic Gradients
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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.
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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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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 (-73.81%)
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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mmflowOpenMMLab optical flow toolbox and benchmark
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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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few-shot-lmThe source code of "Language Models are Few-shot Multilingual Learners" (MRL @ EMNLP 2021)
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