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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/deepmind/leo
공식 코드https://github.com/dekura/llana
공식 코드LLM-Enhanced Bayesian Optimization for Efficient Analog Layout Constraint Generation
https://github.com/derekchen14/mixture_soft_prompts
공식 코드https://github.com/derenlei/CS_Net
공식 코드https://github.com/devavrattomar/sst
공식 코드Self-Supervised Generative Style Transfer for One-Shot Medical Image Segmentation
https://github.com/dfki-nlp/cross-ling-adr
공식 코드https://github.com/diegodoimo/geometry_icl_finetuning
공식 코드The representation landscape of few-shot learning and fine-tuning in large language models
https://github.com/digitalhealthcarelab/25multimodalfoundationmodel
공식 코드https://github.com/dingmyu/DAPN
공식 코드https://github.com/dmis-lab/biobert
공식 코드BioBERT: a pre-trained biomedical language representation model for biomedical text mining
https://github.com/doerlbh/UnsupervisedAttentionMechanism
공식 코드Unsupervised Attention Mechanism across Neural Network Layers
https://github.com/dojunpark/pragmatic_eval_korean
공식 코드Pragmatic Competence Evaluation of Large Language Models for the Korean Language
https://github.com/dongsky/few-shot-vit
공식 코드https://github.com/dragondescentzerotsu/ssm-sam
공식 코드Self-Sampling Meta SAM: Enhancing Few-shot Medical Image Segmentation with Meta-Learning
https://github.com/dual-grp/fedu_fmtl
공식 코드A New Look and Convergence Rate of Federated Multi-Task Learning with Laplacian Regularization
https://github.com/ducminhkhoi/FAPIS
공식 코드https://github.com/ducminhkhoi/Feature-Weighting-and-Boosting
공식 코드https://github.com/dustincarrion/cd-metadl
공식 코드Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification
https://github.com/dut-lujunyu/disagreement
공식 코드https://github.com/dvirsamuel/DRAGON
공식 코드From Generalized zero-shot learning to long-tail with class descriptors
https://github.com/dvlab-research/gfs-seg
공식 코드https://github.com/dvornikita/SUR
공식 코드Selecting Relevant Features from a Multi-domain Representation for Few-shot Classification
https://github.com/dyballa/generalization
공식 코드A separability-based approach to quantifying generalization: which layer is best?
https://github.com/e-bug/iglue
공식 코드IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages
https://github.com/e-bug/volta
공식 코드IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages
https://github.com/eaoltu/hyperseed
공식 코드Hyperseed: Unsupervised Learning with Vector Symbolic Architectures
https://github.com/ebennequin/FewShotVision
공식 코드https://github.com/ebennequin/few-shot-open-set
공식 코드https://github.com/ebennequin/meta-domain-shift
공식 코드Bridging Few-Shot Learning and Adaptation: New Challenges of Support-Query Shift
https://github.com/eccv22-ood-workshop/eccv22-ood-workshop.github.io
공식 코드Advancing Image Retrieval with Few-Shot Learning and Relevance Feedback
https://github.com/edgarschnfld/CADA-VAE-PyTorch
공식 코드Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders
https://github.com/edi-meta-learning/meta-omnium
공식 코드Meta Omnium: A Benchmark for General-Purpose Learning-to-Learn
https://github.com/edwardjhu/TP4
공식 코드https://github.com/egetaga/timepfn
공식 코드TimePFN: Effective Multivariate Time Series Forecasting with Synthetic Data
https://github.com/ekinakyurek/marc
공식 코드The Surprising Effectiveness of Test-Time Training for Few-Shot Learning
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