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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
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
SQL-o1: Automatic Text-to-SQL Program Synthesis via Self-Reward Heuristic Search
point2vec for Self-Supervised Representation Learning on Point Clouds
GLAD: Generalizable Tuning for Vision-Language Models
Doodle Your Keypoints: Sketch-Based Few-Shot Keypoint Detection
An Enhanced Privacy-preserving Federated Few-shot Learning Framework for Respiratory Disease Diagnosis
Few-Shot Learning by Explicit Physics Integration: An Application to Groundwater Heat Transport
ViRefSAM: Visual Reference-Guided Segment Anything Model for Remote Sensing Segmentation
Dynamic Context-Aware Prompt Recommendation for Domain-Specific AI Applications
Ancient Script Image Recognition and Processing: A Review
Universal Music Representations? Evaluating Foundation Models on World Music Corpora
Few-Shot Learning for Industrial Time Series: A Comparative Analysis Using the Example of Screw-Fastening Process Monitoring
The Sample Complexity of Parameter-Free Stochastic Convex Optimization
On The Impact of Merge Request Deviations on Code Review Practices
Hyperbolic Dual Feature Augmentation for Open-Environment
Adapter Naturally Serves as Decoupler for Cross-Domain Few-Shot Semantic Segmentation
Variational Supervised Contrastive Learning
Multi-Distillation from Speech and Music Representation Models
From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models
The Hippocampal Place Field Gradient: An Eigenmode Theory Linking Grid Cell Projections to Multiscale Learning
ViTNF: Leveraging Neural Fields to Boost Vision Transformers in Generalized Category Discovery
Overcoming Data Scarcity in Scanning Tunnelling Microscopy Image Segmentation
Lightweight Relational Embedding in Task-Interpolated Few-Shot Networks for Enhanced Gastrointestinal Disease Classification
Chameleon: A MatMul-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data
From Parameters to Prompts: Understanding and Mitigating the Factuality Gap between Fine-Tuned LLMs
Large Language Models for Solving Economic Dispatch Problem
Hierarchical Material Recognition from Local Appearance
DiSa: Directional Saliency-Aware Prompt Learning for Generalizable Vision-Language Models
MOLE: Metadata Extraction and Validation in Scientific Papers Using LLMs
SaSi: A Self-augmented and Self-interpreted Deep Learning Approach for Few-shot Cryo-ET Particle Detection
Improvement Strategies for Few-Shot Learning in OCT Image Classification of Rare Retinal Diseases
Object-level Cross-view Geo-localization with Location Enhancement and Multi-Head Cross Attention
Clip4Retrofit: Enabling Real-Time Image Labeling on Edge Devices via Cross-Architecture CLIP Distillation
MetaGen Blended RAG: Higher Accuracy for Domain-Specific Q&A Without Fine-Tuning
SD-MAD: Sign-Driven Few-shot Multi-Anomaly Detection in Medical Images
Four Eyes Are Better Than Two: Harnessing the Collaborative Potential of Large Models via Differentiated Thinking and Complementary Ensembles
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.