Papers with Code 아카이브
Few-Shot Learning
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Few-Shot Learning is an example of meta-learning, where a learner is trained on several related tasks, during the meta-training phase, so that it can generalize well to unseen (but related) tasks with just few examples, during the meta-testing phase. An effective approach to the Few-Shot Learning problem is to learn a common representation for various tasks and train task specific classifiers on top of this representation.
Source: Penalty Method for Inversion-Free Deep Bilevel Optimization
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/sambanova/lm-evaluation-harness
https://github.com/sambi97/Low-shot-learning_-Deep-learning-project
https://github.com/samsunglabs/myqasr
FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech
https://github.com/sanixa/CADA-VAE-pytorch
Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders
https://github.com/sanowar-raihan/nerf-meta
https://github.com/sayakpaul/A-Barebones-Image-Retrieval-System
https://github.com/sayakpaul/FunMatch-Distillation
https://github.com/sayef/fsner
https://github.com/schatty/matching-networks-tf
https://github.com/schatty/prototypical-networks-tf
https://github.com/schatty/siamese-networks-tf
https://github.com/scrayish/ML_NLP
https://github.com/seblee97/task_weighted_maml
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/servicenow/synbols
Synbols: Probing Learning Algorithms with Synthetic Datasets
https://github.com/shaohua0116/MultiDigitMNIST
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/shark-nlp/openicl
https://github.com/sheetalreddy/One-shot-learning
https://github.com/sheldonresearch/ProG
All in One and One for All: A Simple yet Effective Method towards Cross-domain Graph Pretraining
https://github.com/sheryl-ai/MetaPred
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/shin-ee-chen/bla
IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages
https://github.com/shmsw25/factscore
https://github.com/shoutOutYangJie/Few-Shot-Adversarial-Learning-for-face-swap
Few-Shot Adversarial Learning of Realistic Neural Talking Head Models
https://github.com/shreyashankar/gpt3-sandbox
https://github.com/shruti-jadon/Covid-19-Detection-Few-Shot-Learning
COVID-19 detection from scarce chest x-ray image data using few-shot deep learning approach
https://github.com/shunzh/pytorch-maml-rl
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/shyamsn97/hyper-nn
HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning
https://github.com/sicara/easy-few-shot-learning
https://github.com/sidney1505/arc_maml_transformer
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/silicx/dlsa
https://github.com/skingorz/prototypeNet-paddle
https://github.com/skytliang/multi-agents-debate
https://github.com/sl-93/SUPERVISED-CONTRASTIVE-LEARNING-FOR-PRE-TRAINED-LANGUAGE-MODEL-FINE-TUNING
Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning
https://github.com/smarton-empower/smarton-ai
https://github.com/smile-data/smile
https://github.com/snorkel-ai/long-context-eval
LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.