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Few-Shot Learning
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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/jdnascim/7set-al
공식 코드Few-shot Learning for Multi-modal Social Media Event Filtering
https://github.com/jeffwillette/cascading_kv_cache
공식 코드Training-Free Exponential Context Extension via Cascading KV Cache
https://github.com/jerrrynie/diverseqa
공식 코드https://github.com/jerrrynie/unsupervised-long-document-qa
공식 코드AttenWalker: Unsupervised Long-Document Question Answering via Attention-based Graph Walking
https://github.com/jhang2020/pcm3
공식 코드Self-Supervised Skeleton-Based Action Representation Learning: A Benchmark and Beyond
https://github.com/jianhuapei/ldm-enabled-semcom-system
공식 코드https://github.com/jianqingzheng/def_diff_rec
공식 코드https://github.com/jianxiangyu/llm4ng
공식 코드Leveraging Large Language Models for Node Generation in Few-Shot Learning on Text-Attributed Graphs
https://github.com/jiaxinchen666/variational-scaling
공식 코드https://github.com/jieshibo/petl-vit
공식 코드Convolutional Bypasses Are Better Vision Transformer Adapters
https://github.com/jihunhamm/bilevel-penalty
공식 코드https://github.com/jimzai/deta
공식 코드https://github.com/jin-s13/unifs
공식 코드UniFS: Universal Few-shot Instance Perception with Point Representations
https://github.com/jinfeihu-stan/ipl
공식 코드Instance-aware Prompt Learning for Language Understanding and Generation
https://github.com/jinhualiang/apt
공식 코드Acoustic Prompt Tuning: Empowering Large Language Models with Audition Capabilities
https://github.com/jlab-nlp/refpydst
공식 코드Diverse Retrieval-Augmented In-Context Learning for Dialogue State Tracking
https://github.com/jlager/few-shot-leaf-segmentation
공식 코드Few-Shot Learning Enables Population-Scale Analysis of Leaf Traits in Populus trichocarpa
https://github.com/jlianglab/ace
공식 코드ACE: Anatomically Consistent Embeddings in Composition and Decomposition
https://github.com/jlinekai/clover
공식 코드Cost-effective Instruction Learning for Pathology Vision and Language Analysis
https://github.com/joakimjohnander/dgpnet
공식 코드https://github.com/johncai117/DAMSL
공식 코드https://github.com/johnmaijer/x-few-_cd-fsod
공식 코드NTIRE 2025 Challenge on Cross-Domain Few-Shot Object Detection: Methods and Results
https://github.com/joyjayng/Bongard-OpenWorld
공식 코드Bongard-OpenWorld: Few-Shot Reasoning for Free-form Visual Concepts in the Real World
https://github.com/jsjs0369/MEBQAT
공식 코드Bitwidth-Adaptive Quantization-Aware Neural Network Training: A Meta-Learning Approach
https://github.com/juglab/DenoiSeg
공식 코드https://github.com/juhongm999/hsnet
공식 코드https://github.com/juliuswang-7/encoderreg
공식 코드Encoding Matching Criteria for Cross-domain Deformable Image Registration
https://github.com/junbao-zhou/track-no-forgetting
공식 코드TeFF: Tracking-enhanced Forgetting-free Few-shot 3D LiDAR Semantic Segmentation
https://github.com/june01/wfsal-icmr21
공식 코드https://github.com/junshern/few-shot-adaptation
공식 코드https://github.com/jwhitehill/compositionalclustering
공식 코드Compositional Clustering: Applications to Multi-Label Object Recognition and Speaker Identification
https://github.com/jzhang38/lpd
공식 코드Better Few-Shot Relation Extraction with Label Prompt Dropout
https://github.com/kabouzeid/point2vec
공식 코드point2vec for Self-Supervised Representation Learning on Point Clouds
https://github.com/kacperkan/conerf
공식 코드https://github.com/kahnchana/opl
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.