SOTAVerified

Few-Shot Learning

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

Papers

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Showing 1951–2000 of 2964 papers

TitleStatusHype
Reinforced Attention for Few-Shot Learning and Beyond—0
Reinforced Few-Shot Acquisition Function Learning for Bayesian Optimization—0
Relational Generalized Few-Shot Learning—0
RPT: Relational Pre-trained Transformer Is Almost All You Need towards Democratizing Data Preparation—0
Remember the Difference: Cross-Domain Few-Shot Semantic Segmentation via Meta-Memory Transfer—0
ReMP: Rectified Metric Propagation for Few-Shot Learning—0
Representation based and Attention augmented Meta learning—0
Representation Change in Model-Agnostic Meta-Learning—0
Representation learning from videos in-the-wild: An object-centric approach—0
Representing Brain Anatomical Regularity and Variability by Few-Shot Embedding—0
Reproducibility and Stability Analysis in Metric-Based Few-Shot Learning—0
Re-ranking for image retrieval and transductive few-shot classification—0
Prior-Knowledge and Attention-based Meta-Learning for Few-Shot Learning—0
Rethinking ASTE: A Minimalist Tagging Scheme Alongside Contrastive Learning—0
Rethinking Few-Shot Medical Image Segmentation by SAM2: A Training-Free Framework with Augmentative Prompting and Dynamic Matching—0
Rethinking the Metric in Few-shot Learning: From an Adaptive Multi-Distance Perspective—0
Rethinking Video Anomaly Detection - A Continual Learning Approach—0
Rethink the Effectiveness of Text Data Augmentation: An Empirical Analysis—0
Retrieval-Enhanced Visual Prompt Learning for Few-shot Classification—0
Retrieval meets Long Context Large Language Models—0
Retrieving Versus Understanding Extractive Evidence in Few-Shot Learning—0
Retrofitting Light-weight Language Models for Emotions using Supervised Contrastive Learning—0
Beyond DAGs: A Latent Partial Causal Model for Multimodal Learning—0
Review of Zero-Shot and Few-Shot AI Algorithms in The Medical Domain—0
Re-ViLM: Retrieval-Augmented Visual Language Model for Zero and Few-Shot Image Captioning—0
Supervised Momentum Contrastive Learning for Few-Shot Classification—0
Revisiting Distance Metric Learning for Few-Shot Natural Language Classification—0
Revisiting Few-Shot Learning for Facial Expression Recognition—0
Revisiting Few-shot Relation Classification: Evaluation Data and Classification Schemes—0
Revisiting Fine-tuning for Few-shot Learning—0
Revisiting Learnable Affines for Batch Norm in Few-Shot Transfer Learning—0
Revisiting Linear Decision Boundaries for Few-Shot Learning with Transformer Hypernetworks—0
Revisiting Meta-Learning as Supervised Learning—0
Revisiting Metric Learning for Few-Shot Image Classification—0
Revisiting Mid-Level Patterns for Cross-Domain Few-Shot Recognition—0
Revisiting the Exit from Nuclear Energy in Germany with NLP—0
Packet Inspection Transformer: A Self-Supervised Journey to Unseen Malware Detection with Few Samples—0
RGL: A Simple yet Effective Relation Graph Augmented Prompt-based Tuning Approach for Few-Shot Learning—0
Rich Semantics Improve Few-shot Learning—0
Riemannian Geometric-based Meta Learning—0
RmGPT: Rotating Machinery Generative Pretrained Model—0
RM-PoT: Reformulating Mathematical Problems and Solving via Program of Thoughts—0
RNNP: A Robust Few-Shot Learning Approach—0
RoboGrasp: A Universal Grasping Policy for Robust Robotic Control—0
Robust Cross-Modal Semi-supervised Few Shot Learning—0
Robust Internal Representations for Domain Generalization—0
Robust Meta-learning with Noise via Eigen-Reptile—0
Robust Task Clustering for Deep Many-Task Learning—0
Robust Transductive Few-shot Learning via Joint Message Passing and Prototype-based Soft-label Propagation—0
RPLKG: Robust Prompt Learning with Knowledge Graph—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1gpt-4-0125-previewAccuracy61.91—Unverified
2gpt-4-0125-previewAccuracy52.49—Unverified
3gpt-3.5-turboAccuracy41.48—Unverified
4gpt-3.5-turboAccuracy37.06—Unverified
5johnsnowlabs/JSL-MedMNX-7BAccuracy25.63—Unverified
6yikuan8/Clinical-LongformerAccuracy25.55—Unverified
7BioMistral/BioMistral-7B-DAREAccuracy25.06—Unverified
8yikuan8/Clinical-LongformerAccuracy25.04—Unverified
9PharMolix/BioMedGPT-LM-7BAccuracy24.92—Unverified
10PharMolix/BioMedGPT-LM-7BAccuracy24.75—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean67.27—Unverified
2SaSPA + CAL4-shot Accuracy48.3—Unverified
3Real-Guidance + CAL4-shot Accuracy41.5—Unverified
4CAL4-shot Accuracy40.9—Unverified
#ModelMetricClaimedVerifiedStatus
1SaSPA + CALHarmonic mean52.2—Unverified
2CALHarmonic mean35.2—Unverified
3Variational Prompt TuningHarmonic mean34.69—Unverified
4Real-Guidance + CALHarmonic mean34.5—Unverified
#ModelMetricClaimedVerifiedStatus
1BGNNAccuracy92.7—Unverified
2TIM-GDAccuracy87.4—Unverified
3UNEM-GaussianAccuracy66.4—Unverified
#ModelMetricClaimedVerifiedStatus
1EASY (transductive)Accuracy82.75—Unverified
2HCTransformers5 way 1~2 shot74.74—Unverified
3HyperShotAccuracy53.18—Unverified
#ModelMetricClaimedVerifiedStatus
1SaSPA + CAL4-shot Accuracy66.7—Unverified
2Real-Guidance + CAL4-shot Accuracy44.3—Unverified
3CAL4-shot Accuracy42.2—Unverified
#ModelMetricClaimedVerifiedStatus
1HCTransformersAcc74.74—Unverified
2DPGNAcc67.6—Unverified
#ModelMetricClaimedVerifiedStatus
1MetaGen Blended RAG (zero-shot)Accuracy77.9—Unverified
2CoT-T5-11B (1024 Shot)Accuracy73.42—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean96.44—Unverified
#ModelMetricClaimedVerifiedStatus
1CoT-T5-11B (1024 Shot)Accuracy68.3—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean77.71—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean81.12—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean91.57—Unverified
#ModelMetricClaimedVerifiedStatus
1CovidExpertAUC-ROC1—Unverified
#ModelMetricClaimedVerifiedStatus
1CoT-T5-11B (1024 Shot)Accuracy78.02—Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy65.7—Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy73.2—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean96.82—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean73.07—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean78.51—Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy52.3—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean79—Unverified