https://github.com/dragen1860/MAML-Pytorch
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Papers with Code 아카이브
Few-Shot Learning
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- 평가표
- 27
- 보존된 평가 기록
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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/dragen1860/MAML-Pytorch-RL
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/dragen1860/Reptile-Pytorch
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/dsapandora/s_cera
https://github.com/dvlab-research/pfenet
Prior Guided Feature Enrichment Network for Few-Shot Segmentation
https://github.com/dvornikita/fewshot_ensemble
Diversity with Cooperation: Ensemble Methods for Few-Shot Classification
https://github.com/eavnjeong/ief-vad
Expanding Event Modality Applications through a Robust CLIP-Based Encoder
https://github.com/ebadrian/metadl
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/ecnu-icalk/educhat
https://github.com/ecolab-postech/owq
https://github.com/edisutoyo/satd-augmentation
https://github.com/eduard626/interaction-tensor-affordances
https://github.com/eezywu/PARN
PARN: Position-Aware Relation Networks for Few-Shot Learning
https://github.com/emrgnt-cmplxty/zero-shot-replication
https://github.com/erikalu/class-agnostic-counting
https://github.com/ethz-privsec/superhuman-ai-consistency
https://github.com/ethz-spylab/superhuman-ai-consistency
https://github.com/exelents/soft-prompt-tuning
https://github.com/fabian57fabian/prototypical-networks-few-shot-learning
https://github.com/facebookresearch/anli
https://github.com/facebookresearch/chai
https://github.com/facebookresearch/disentangling-correlated-factors
Learning Deep Disentangled Embeddings with the F-Statistic Loss
https://github.com/facebookresearch/low-shot-shrink-hallucinate
https://github.com/facebookresearch/metaclip
LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs
https://github.com/facebookresearch/quip
https://github.com/facico/chinese-vicuna
https://github.com/fajri91/indommlu
https://github.com/fangpin/siamese-network
https://github.com/farhanhubble/omniglot
https://github.com/farinamatteo/rethinking_fewshot_vlms
https://github.com/fastforwardlabs/learning-to-learn
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/few-shot-NER-benchmark/BaselineCode
https://github.com/flagalpha/llama2-chinese
https://github.com/flexibility2/Pytorch-Implementation-for-RelationNet
https://github.com/floodsung/LearningToCompare_FSL
https://github.com/fmu2/PyTorch-MAML
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.