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/royalskye/omni-vrp

공식 코드

Towards Omni-generalizable Neural Methods for Vehicle Routing Problems

https://github.com/roysoumya/genemask

공식 코드

GeneMask: Fast Pretraining of Gene Sequences to Enable Few-Shot Learning

https://github.com/rpeloff/multimodal-one-shot-learning

공식 코드

Multimodal One-Shot Learning of Speech and Images

https://github.com/rpnode-fss/rpnode_fss

공식 코드

Robust Prototypical Few-Shot Organ Segmentation with Regularized Neural-ODEs

https://github.com/rrmenon10/ADAPET

공식 코드

Improving and Simplifying Pattern Exploiting Training

https://github.com/rsamf/text-guided-mixup

공식 코드

Text-Guided Mixup Towards Long-Tailed Image Categorization

https://github.com/rshaojimmy/AAAI2020-RFMetaFAS

공식 코드

Regularized Fine-grained Meta Face Anti-spoofing

https://github.com/rsheth80/pmf-automl

공식 코드

Probabilistic Matrix Factorization for Automated Machine Learning

https://github.com/rtmaww/EntLM

공식 코드

Template-free Prompt Tuning for Few-shot NER

https://github.com/rucaibox/recbole2.0

공식 코드

RecBole 2.0: Towards a More Up-to-Date Recommendation Library

https://github.com/rudylyh/CTN_data

공식 코드

Contour Transformer Network for One-shot Segmentation of Anatomical Structures

https://github.com/ruihuili/MetaCC

공식 코드

A Channel Coding Benchmark for Meta-Learning

https://github.com/runnanchen/clip2scene

공식 코드

CLIP2Scene: Towards Label-efficient 3D Scene Understanding by CLIP

https://github.com/ruohliuq/fedmeta

공식 코드

Learning Task-Aware Energy Disaggregation: a Federated Approach

https://github.com/ruthless-man/awesome-learn-from-model

공식 코드

Learn From Model Beyond Fine-Tuning: A Survey

https://github.com/ruxie189/wss_pole

공식 코드

Prompting classes: Exploring the Power of Prompt Class Learning in Weakly Supervised Semantic Segmentation

https://github.com/rvinas/polythetic_metalearning

공식 코드

Attentional Meta-learners for Few-shot Polythetic Classification

https://github.com/ryushinn/meta-sampling

공식 코드

Learning to Learn and Sample BRDFs

https://github.com/s-a-malik/multi-few

공식 코드

Multi-Modal Fusion by Meta-Initialization

https://github.com/s-l-chausson/easyclaimsdetection

공식 코드

Detecting Statements in Text: A Domain-Agnostic Few-Shot Solution

https://github.com/s4nyam/ramario

공식 코드

RAMario: Experimental Approach to Reptile Algorithm -- Reinforcement Learning for Mario

https://github.com/saakethmm/nc-prototypical-networks

공식 코드

On the Role of Neural Collapse in Meta Learning Models for Few-shot Learning

https://github.com/saic-fi/bayesian-prompt-learning

공식 코드

Bayesian Prompt Learning for Image-Language Model Generalization

https://github.com/saikat107/natgen

공식 코드

NatGen: Generative pre-training by "Naturalizing" source code

https://github.com/sail-sg/optim4rl

공식 코드

Learning to Optimize for Reinforcement Learning

https://github.com/saketdingliwal/Few-Shot-DST

공식 코드

Few Shot Dialogue State Tracking using Meta-learning

https://github.com/salehiac/FAERY-original

공식 코드

Few-shot Quality-Diversity Optimization

https://github.com/salesforce/DNNC-few-shot-intent

공식 코드

Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language Inference

https://github.com/salesforce/DialogStudio

공식 코드

DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI

https://github.com/salesforce/deeptime

공식 코드

Learning Deep Time-index Models for Time Series Forecasting

https://github.com/sam1224/aenet

공식 코드

Eliminating Feature Ambiguity for Few-Shot Segmentation

https://github.com/sam1224/hmnet

공식 코드

Hybrid Mamba for Few-Shot Segmentation

https://github.com/sam1224/sccan

공식 코드

Self-Calibrated Cross Attention Network for Few-Shot Segmentation

https://github.com/samsunglabs/swissdino

공식 코드

Swiss DINO: Efficient and Versatile Vision Framework for On-device Personal Object Search

https://github.com/samuelhess/mll_fsl

공식 코드

A Maximum Log-Likelihood Method for Imbalanced Few-Shot Learning Tasks

https://github.com/samuelstevens/mindthegap

공식 코드

Mind the Gap: Evaluating Vision Systems in Small Data Applications

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