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Search results for paper few shot learning
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16 search results found
Transferlearning
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12,494
Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习
Awesome Domain Adaptation
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4,910
A collection of AWESOME things about domian adaptation
Decryptprompt
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1,655
总结Prompt&LLM论文,开源数据&模型,AIGC应用
Fsl Mate
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1,611
FSL-Mate: A collection of resources for few-shot learning (FSL).
Awesome Meta Learning
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917
A curated list of Meta Learning papers, code, books, blogs, videos, datasets and other resources.
Awesome Papers Fewshot
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877
Collection for Few-shot Learning
Lm Bff
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550
ACL'2021: LM-BFF: Better Few-shot Fine-tuning of Language Models https://arxiv.org/abs/2012.15723
Awesome Few Shot Image Generation
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295
A curated list of papers, code and resources pertaining to few-shot image generation.
Metric Learning Divide And Conquer
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213
Source code for the paper "Divide and Conquer the Embedding Space for Metric Learning", CVPR 2019
Deep Kernel Transfer
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142
Official pytorch implementation of the paper "Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels" (NeurIPS 2020)
Tim
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109
(NeurIPS 2020) Transductive Information Maximization for Few-Shot Learning https://arxiv.org/abs/2008.11297
Low Resource Kepapers
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93
A Paper List of Low-resource Information Extraction
Deviation Network
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64
Source code of the KDD19 paper "Deep anomaly detection with deviation networks", weakly/partially supervised anomaly detection, few-shot anomaly detection
F2gan Few Shot Image Generation
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41
Fusing-and-Filling GAN (F2GAN) for few-shot image generation, ACM MM2020
Cluttered Omniglot
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35
Cluttered Omniglot dataset and models
Awesome Few Shot
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27
Awesome Few-shot learning
Few_shot_learning
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21
Awesome papers in few-shot learning/one-shot learning.
L2f
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15
Source code for CVPR 2020 paper "Learning to Forget for Meta-Learning"
Timehetnet
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10
Learning complex time series forecasting models usually requires a large amount of data, as each model is trained from scratch for each task/data set. Leveraging learning experience with similar datasets is a well-established technique for classification problems called few-shot classification. However, existing approaches cannot be applied to time-series forecasting because i) multivariate time-series datasets have different channels and ii) forecasting is principally different from classificat
Contextual Squeeze And Excitation
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10
Official Pytorch implementation of the paper "Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification" (NeurIPS 2022)
Meta Learning Study
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5
Deepest Season 6 Meta-Learning study papers plus alpha
Few Shot Learning
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5
Curated List of Few shot and One shot Learning Papers and resources
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