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Few-Shot Learning

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하위 태스크: 12
평가표: 27
보존된 평가 기록: 52

Few-Shot Learning is an example of meta-learning, where a learner is trained on several related tasks, during the meta-training phase, so that it can generalize well to unseen (but related) tasks with just few examples, during the meta-testing phase. An effective approach to the Few-Shot Learning problem is to learn a common representation for various tasks and train task specific classifiers on top of this representation.

Source: Penalty Method for Inversion-Free Deep Bilevel Optimization

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/shannonai/knn-ner

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$k$NN-NER: Named Entity Recognition with Nearest Neighbor Search

https://github.com/shiluohe/referencetrustabledecoding

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Reference Trustable Decoding: A Training-Free Augmentation Paradigm for Large Language Models

https://github.com/shiyuanh/tane

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Task-Adaptive Negative Envision for Few-Shot Open-Set Recognition

https://github.com/shlee625/cxgrad

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Contextual Gradient Scaling for Few-Shot Learning

https://github.com/shmsw25/Channel-LM-Prompting

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Noisy Channel Language Model Prompting for Few-Shot Text Classification

https://github.com/shmulvad/zero-for-ner

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Zero-Shot Learning in Named-Entity Recognition with External Knowledge

https://github.com/showlab/sct

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SCT: A Simple Baseline for Parameter-Efficient Fine-Tuning via Salient Channels

https://github.com/shruti-jadon/Covid-19-Detection

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COVID-19 detection from scarce chest x-ray image data using few-shot deep learning approach

https://github.com/shruti-jadon/Hands-on-One-Shot-Learning

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An Overview of Deep Learning Architectures in Few-Shot Learning Domain

https://github.com/shruti-jadon/PlantsDiseaseDetection

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SSM-Net for Plants Disease Identification in Low Data Regime

https://github.com/shruti-jadon/Siamese-Network-for-One-shot-Learning

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Improving Siamese Networks for One Shot Learning using Kernel Based Activation functions

https://github.com/shuailyu0110/sql-o1

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SQL-o1: Automatic Text-to-SQL Program Synthesis via Self-Reward Heuristic Search

https://github.com/shuguoj/hsi-classification

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A Survey: Deep Learning for Hyperspectral Image Classification with Few Labeled Samples

https://github.com/shuvenduroy/coprompt

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Consistency-guided Prompt Learning for Vision-Language Models

https://github.com/shwangtangjun/Diff-ResNet

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Diffusion Mechanism in Residual Neural Network: Theory and Applications

https://github.com/siddhantjain/PointCloudAnnotationTool

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Few-Shot Point Cloud Region Annotation with Human in the Loop

https://github.com/silverbottlep/meta_curvature

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https://github.com/simonguiroy/abe

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Improving Meta-Learning Generalization with Activation-Based Early-Stopping

https://github.com/sinahmr/diam

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A Strong Baseline for Generalized Few-Shot Semantic Segmentation

https://github.com/skingorz/ggiu

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Gestalt-Guided Image Understanding for Few-Shot Learning

https://github.com/smappnyu/dascl

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Dictionary-Assisted Supervised Contrastive Learning

https://github.com/smufang/fewshotgraph

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A Survey of Few-Shot Learning on Graphs: from Meta-Learning to Pre-Training and Prompt Learning

https://github.com/snap-stanford/comet

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Concept Learners for Few-Shot Learning

https://github.com/social-ai-studio/mod_hate

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Modularized Networks for Few-shot Hateful Meme Detection

https://github.com/sohiniroych/Paralllel-ESN-For-Image-Quality

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Few Shot Learning Framework to Reduce Inter-observer Variability in Medical Images

https://github.com/solarlee/fewsar

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FewSAR: A Few-shot SAR Image Classification Benchmark

https://github.com/songw-sw/f2l

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Federated Few-shot Learning

https://github.com/songw-sw/faith

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FAITH: Few-Shot Graph Classification with Hierarchical Task Graphs

https://github.com/songw-sw/glitter

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Graph Few-shot Learning with Task-specific Structures

https://github.com/songw-sw/tent

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Task-Adaptive Few-shot Node Classification

https://github.com/spearsheep/APT-Architectural-Planning-LLM-Agent

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APT: Architectural Planning and Text-to-Blueprint Construction Using Large Language Models for Open-World Agents

https://github.com/spraise/BSNet

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BSNet: Bi-Similarity Network for Few-shot Fine-grained Image Classification

https://github.com/srzer/LaMo-2023

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Unleashing the Power of Pre-trained Language Models for Offline Reinforcement Learning

https://github.com/start2020/awesome-timeseries-llm-fm

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A Survey of Time Series Foundation Models: Generalizing Time Series Representation with Large Language Model

https://github.com/stefanowoerner/medimeta-fsl-benchmark

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Navigating Data Scarcity using Foundation Models: A Benchmark of Few-Shot and Zero-Shot Learning Approaches in Medical Imaging

https://github.com/stepjam/RLBench

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RLBench: The Robot Learning Benchmark & Learning Environment

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