Papers with Code 아카이브

Meta-Learning

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하위 태스크: 3
평가표: 4
보존된 평가 기록: 13

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/yangyongjin/melo

공식 코드

Meta-Learning with Adaptive Weighted Loss for Imbalanced Cold-Start Recommendation

https://github.com/yangyuwenyang/Codes-for-Deep-Transfer-Learning-Based-Downlink-Channel-Prediction-for-FDD-Massive-MIMO-Systems

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Deep Transfer Learning Based Downlink Channel Prediction for FDD Massive MIMO Systems

https://github.com/yanhaojin/generalized-ridge-regression-for-meta-learning

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Meta-Learning with Generalized Ridge Regression: High-dimensional Asymptotics, Optimality and Hyper-covariance Estimation

https://github.com/yaniker/one-shot-skill-assessment-in-high-stakes-domains-with-limited-data-via-meta-learning

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One-shot skill assessment in high-stakes domains with limited data via meta learning

https://github.com/yanzihan1/psml

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Towards Improving Embedding Based Models of Social Network Alignment via Pseudo Anchors

https://github.com/yaoyao-liu/E3BM

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An Ensemble of Epoch-wise Empirical Bayes for Few-shot Learning

https://github.com/yaoyao-liu/meta-transfer-learning

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Meta-Transfer Learning for Few-Shot Learning

https://github.com/yassouali/SCL

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Spatial Contrastive Learning for Few-Shot Classification

https://github.com/yazhouzhu19/rpt

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Few-Shot Medical Image Segmentation via a Region-enhanced Prototypical Transformer

https://github.com/ybendou/easy

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EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients

https://github.com/ybendou/fs-generalization

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A Statistical Model for Predicting Generalization in Few-Shot Classification

https://github.com/ybendou/fs-text2stats

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Inferring Latent Class Statistics from Text for Robust Visual Few-Shot Learning

https://github.com/ybseo-academy/TAALM

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Train-Attention: Meta-Learning Where to Focus in Continual Knowledge Learning

https://github.com/ybzh/point-dae

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Point-DAE: Denoising Autoencoders for Self-supervised Point Cloud Learning

https://github.com/ycfate/id-like

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ID-like Prompt Learning for Few-Shot Out-of-Distribution Detection

https://github.com/ycm094/mg-fte-main

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Mutually Guided Few-shot Learning for Relational Triple Extraction

https://github.com/yczhou001/ClarET

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ClarET: Pre-training a Correlation-Aware Context-To-Event Transformer for Event-Centric Generation and Classification

https://github.com/ydu-uva/hiermemory

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Hierarchical Variational Memory for Few-shot Learning Across Domains

https://github.com/ydu-uva/protodiff

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ProtoDiff: Learning to Learn Prototypical Networks by Task-Guided Diffusion

https://github.com/yfguo91/neuroclip

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NeuroCLIP: Neuromorphic Data Understanding by CLIP and SNN

https://github.com/yhu01/PT-MAP

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Leveraging the Feature Distribution in Transfer-based Few-Shot Learning

https://github.com/yhu01/bms

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Squeezing Backbone Feature Distributions to the Max for Efficient Few-Shot Learning

https://github.com/yilun-wu/MetaL-Benchmark

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ALPaCA vs. GP-based Prior Learning: A Comparison between two Bayesian Meta-Learning Algorithms

https://github.com/yilunzhao/robut

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RobuT: A Systematic Study of Table QA Robustness Against Human-Annotated Adversarial Perturbations

https://github.com/yinboc/few-shot-meta-baseline

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Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning

https://github.com/yinboc/trans-inr

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Transformers as Meta-Learners for Implicit Neural Representations

https://github.com/yingutk/NL-MAT

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Non-Local Representation based Mutual Affine-Transfer Network for Photorealistic Stylization

https://github.com/yinzhicun/metaf2n

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MetaF2N: Blind Image Super-Resolution by Learning Efficient Model Adaptation from Faces

https://github.com/yiren-jian/MetaPix

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MetaPix: Domain Transfer for Semantic Segmentation by Meta Pixel Weighting

https://github.com/yiren-jian/labelhalluc

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Label Hallucination for Few-Shot Classification

https://github.com/yitewang/metantk-nas

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Global Convergence of MAML and Theory-Inspired Neural Architecture Search for Few-Shot Learning

https://github.com/yiweilu3/Few-shot-Scene-adaptive-Anomaly-Detection

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Few-shot Scene-adaptive Anomaly Detection

https://github.com/yixianqianzy/dpa

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Diverse Preference Augmentation with Multiple Domains for Cold-start Recommendations

https://github.com/yizhouzhao/tangram

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Learning from the Tangram to Solve Mini Visual Tasks

https://github.com/yjwang0618/subgoal-based-exploration

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Dynamic Subgoal-based Exploration via Bayesian Optimization

https://github.com/yjyddq/DLIF

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Generalized Face Anti-spoofing via Finer Domain Partition and Disentangling Liveness-irrelevant Factors

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