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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/fsna2022/fsna

Frustratingly Simple Few-Shot Object Detection

https://github.com/fsoft-ai4code/codecapybara

LLaMA: Open and Efficient Foundation Language Models

https://github.com/fujenchu/matchingNet

Matching Networks for One Shot Learning

https://github.com/futureisatyourhand/Top-Related-Meta-Learning-Method-for-Few-Shot-Detection

Top-Related Meta-Learning Method for Few-Shot Object Detection

https://github.com/fywalter/label-bias

Language Models are Few-Shot Learners

https://github.com/gabrielhuang/centroid-networks

Are Few-Shot Learning Benchmarks too Simple ? Solving them without Task Supervision at Test-Time

https://github.com/gabrielhuang/reptile-pytorch

On First-Order Meta-Learning Algorithms

https://github.com/ganjinzero/rrhf

LLaMA: Open and Efficient Foundation Language Models

https://github.com/gaobb/MetaUAS

MetaUAS: Universal Anomaly Segmentation with One-Prompt Meta-Learning

https://github.com/gaopengcuhk/tip-adapter

Prompt, Generate, then Cache: Cascade of Foundation Models makes Strong Few-shot Learners

https://github.com/garyCC227/thesis

Adapted Deep Embeddings: A Synthesis of Methods for $k$-Shot Inductive Transfer Learning

https://github.com/gbrltv/business_process_encoding

Characterizing classification datasets: a study of meta-features for meta-learning

https://github.com/gcucurull/maml_flax

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/gebob19/REPTILE-Metalearning

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/gebob19/cscd94-metalearning

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/gebob19/cscd94_metalearning

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/generation21/generation6011

Self-Supervised Generalisation with Meta Auxiliary Learning

https://github.com/geopanag/pandemic_tgnn

Transfer Graph Neural Networks for Pandemic Forecasting

https://github.com/georgosgeorgos/few-shot-diffusion-models

Few-Shot Diffusion Models

https://github.com/ggerganov/llama.cpp

Language Models are Few-Shot Learners

https://github.com/ggml-org/llama.cpp

Language Models are Few-Shot Learners

https://github.com/giovanniguidi/logo-detection

A Deep One-Shot Network for Query-based Logo Retrieval

https://github.com/gitaar9/MLDAGAN

Data Augmentation Generative Adversarial Networks

https://github.com/gitabcworld/ConvArc

Attentive Recurrent Comparators

https://github.com/gjp1203/LIV360SV

IDD: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained Environments

https://github.com/glehet/ttm1

Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series

https://github.com/gmum/dl-mo-2021

Language Models are Few-Shot Learners

https://github.com/gomerudo/nas-dmrl

Learning to reinforcement learn

https://github.com/gomerudo/openai-baselines

Learning to reinforcement learn

https://github.com/google-deepmind/emergent_in_context_learning

Data Distributional Properties Drive Emergent In-Context Learning in Transformers

https://github.com/google/paxml

PaLM: Scaling Language Modeling with Pathways

https://github.com/goolulusaurs/WorldModels

Gotta Learn Fast: A New Benchmark for Generalization in RL

https://github.com/gpt4life/alpagasus

GPT-4 Technical Report

https://github.com/grantslatton/llama.cpp

Language Models are Few-Shot Learners

https://github.com/grasp-lyrl/low-dimensional-deepnets

A picture of the space of typical learnable tasks

https://github.com/greenbitai/low_bit_llama

LLaMA: Open and Efficient Foundation Language Models

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.