https://github.com/jaulin-bage/task-adapter-pp
공식 코드Task-Adapter++: Task-specific Adaptation with Order-aware Alignment for Few-shot Action Recognition
Papers with Code 아카이브
Meta-Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Meta-learning is a methodology considered with "learning to learn" machine learning algorithms.
<span style="color:grey; opacity: 0.6">( Image credit: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/jaychempan/ETS
공식 코드https://github.com/jayelm/lsl
공식 코드Shaping Visual Representations with Language for Few-shot Classification
https://github.com/jd-coderepos/proc-tm
공식 코드https://github.com/jdnascim/7set-al
공식 코드Few-shot Learning for Multi-modal Social Media Event Filtering
https://github.com/jdwillard19/MTL_lakes
공식 코드Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning
https://github.com/jeankaddour/paml
공식 코드https://github.com/jeffwillette/cascading_kv_cache
공식 코드Training-Free Exponential Context Extension via Cascading KV Cache
https://github.com/jennyzzt/dgm
공식 코드Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents
https://github.com/jeremyxsc/dmf
공식 코드Deep Multimodal Fusion for Generalizable Person Re-identification
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/jhngjng/naq-pytorch
공식 코드https://github.com/jiachenwestlake/oodpl
공식 코드Generalizing Reward Modeling for Out-of-Distribution Preference Learning
https://github.com/jiangmengli/metamask
공식 코드MetaMask: Revisiting Dimensional Confounder for Self-Supervised Learning
https://github.com/jiangmengli/metaug
공식 코드https://github.com/jiangwenj02/curvenet-v1
공식 코드Delving into Sample Loss Curve to Embrace Noisy and Imbalanced Data
https://github.com/jiangwenj02/dynamic_loss
공식 코드https://github.com/jianhuapei/ldm-enabled-semcom-system
공식 코드https://github.com/jiank2/fate
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/adaptationagnosticmetalearning
공식 코드https://github.com/jiaxinchen666/variational-scaling
공식 코드https://github.com/jieshibo/petl-vit
공식 코드Convolutional Bypasses Are Better Vision Transformer Adapters
https://github.com/jihoontack/SiMT
공식 코드https://github.com/jihoontack/mac
공식 코드Online Adaptation of Language Models with a Memory of Amortized Contexts
https://github.com/jihunhamm/bilevel-penalty
공식 코드https://github.com/jimengshi/refine
공식 코드ReFine: Boosting Time Series Prediction of Extreme Events by Reweighting and Fine-tuning
https://github.com/jimzai/deta
공식 코드https://github.com/jin-s13/unifs
공식 코드UniFS: Universal Few-shot Instance Perception with Point Representations
https://github.com/jinbo-hu/seld-data-generator
공식 코드https://github.com/jindongwang/transferlearning
공식 코드Exploiting Adapters for Cross-lingual Low-resource Speech Recognition
https://github.com/jindongwang/transferlearning/tree/master/code/ASR
공식 코드Exploiting Adapters for Cross-lingual Low-resource Speech Recognition
https://github.com/jinfeihu-stan/ipl
공식 코드Instance-aware Prompt Learning for Language Understanding and Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.