https://github.com/brjathu/SKD
공식 코드Self-supervised Knowledge Distillation for Few-shot Learning
Papers with Code 아카이브
Meta-Learning
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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/brjathu/iTAML
공식 코드https://github.com/btnorman/First-Explore
공식 코드First-Explore, then Exploit: Meta-Learning to Solve Hard Exploration-Exploitation Trade-Offs
https://github.com/buddi-ai/ciams
공식 코드Clustering Indices based Automatic Classification Model Selection
https://github.com/bugrabaran/semantics-driven-fsl
공식 코드Semantics-driven Attentive Few-shot Learning over Clean and Noisy Samples
https://github.com/buoncubi/fuzzy_sit
공식 코드https://github.com/bupt-ai-cz/Meta-SelfLearning
공식 코드Meta Self-Learning for Multi-Source Domain Adaptation: A Benchmark
https://github.com/bupt-ai-cz/restnet
공식 코드RestNet: Boosting Cross-Domain Few-Shot Segmentation with Residual Transformation Network
https://github.com/buptxiaofeng/proxynet_fsl
공식 코드https://github.com/buswayne/meta-glis
공식 코드Learning Low-Dimensional Embeddings for Black-Box Optimization
https://github.com/byronji/cellclat
공식 코드https://github.com/byronji/drgcl
공식 코드Rethinking Dimensional Rationale in Graph Contrastive Learning from Causal Perspective
https://github.com/bytedance/GR-MG
공식 코드GR-MG: Leveraging Partially Annotated Data via Multi-Modal Goal-Conditioned Policy
https://github.com/bytefuse/mamlcon
공식 코드Mitigating Catastrophic Forgetting for Few-Shot Spoken Word Classification Through Meta-Learning
https://github.com/c-juhwan/unigen
공식 코드https://github.com/c4dm/dcase-few-shot-bioacoustic
공식 코드Few-shot bioacoustic event detection at the DCASE 2023 challenge
https://github.com/caesarea38/doclangid
공식 코드DocLangID: Improving Few-Shot Training to Identify the Language of Historical Documents
https://github.com/callanwu/star
공식 코드https://github.com/callmesanfornow/fsl-audio-abuse
공식 코드Towards Cross-Lingual Audio Abuse Detection in Low-Resource Settings with Few-Shot Learning
https://github.com/calvinyeungck/automated_assignment_grading
공식 코드A Zero-Shot LLM Framework for Automatic Assignment Grading in Higher Education
https://github.com/cambridge-mlg/LITE
공식 코드https://github.com/cambridge-mlg/cnaps
공식 코드Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes
https://github.com/cambridge-mlg/dpconvcnp
공식 코드Noise-Aware Differentially Private Regression via Meta-Learning
https://github.com/cambridge-mlg/fit
공식 코드https://github.com/cambridge-mlg/tetnp
공식 코드https://github.com/cambridgeltl/prompt4bli
공식 코드https://github.com/cambridgeltl/sail-bli
공식 코드Self-Augmented In-Context Learning for Unsupervised Word Translation
https://github.com/came-thu/flamepinn-1d
공식 코드https://github.com/camma-public/peskavlp
공식 코드Learning Multi-modal Representations by Watching Hundreds of Surgical Video Lectures
https://github.com/camma-public/surgvlp
공식 코드Learning Multi-modal Representations by Watching Hundreds of Surgical Video Lectures
https://github.com/canyuchen/promptda
공식 코드PromptDA: Label-guided Data Augmentation for Prompt-based Few-shot Learners
https://github.com/caoji2001/hoser
공식 코드Holistic Semantic Representation for Navigational Trajectory Generation
https://github.com/cbfinn/maml
공식 코드Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/cbfinn/maml_rl
공식 코드Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/cc13qq/cad
공식 코드https://github.com/cea-list/metamtreg
공식 코드Improving Few-Shot Learning through Multi-task Representation Learning Theory
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.