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

Showing 501550 of 2964 papers

TitleStatusHype
MAML is a Noisy Contrastive Learner in ClassificationCode1
Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language ModelsCode1
Mutual-Information Based Few-Shot ClassificationCode1
Unsupervised Embedding Adaptation via Early-Stage Feature Reconstruction for Few-Shot ClassificationCode1
EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter OptimizationCode1
Transductive Few-Shot Learning: Clustering is All You Need?Code1
Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled DataCode1
Learning to Affiliate: Mutual Centralized Learning for Few-shot ClassificationCode1
DETReg: Unsupervised Pretraining with Region Priors for Object DetectionCode1
Anti-aliasing Semantic Reconstruction for Few-Shot Semantic SegmentationCode1
Effect of Pre-Training Scale on Intra- and Inter-Domain Full and Few-Shot Transfer Learning for Natural and Medical X-Ray Chest ImagesCode1
Bridging Few-Shot Learning and Adaptation: New Challenges of Support-Query ShiftCode1
True Few-Shot Learning with Language ModelsCode1
Intriguing Properties of Vision TransformersCode1
Few-Shot Learning by Integrating Spatial and Frequency RepresentationCode1
Representation Learning via Global Temporal Alignment and Cycle-ConsistencyCode1
Learning Implicit Temporal Alignment for Few-shot Video ClassificationCode1
Diffusion Mechanism in Residual Neural Network: Theory and ApplicationsCode1
Few-Shot Video Object DetectionCode1
Entailment as Few-Shot LearnerCode1
UVStyle-Net: Unsupervised Few-shot Learning of 3D Style Similarity Measure for B-RepsCode1
Relational Learning with Gated and Attentive Neighbor Aggregator for Few-Shot Knowledge Graph CompletionCode1
Mutual Contrastive Learning for Visual Representation LearningCode1
Non-Parametric Few-Shot Learning for Word Sense DisambiguationCode1
The Power of Scale for Parameter-Efficient Prompt TuningCode1
CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLPCode1
Meta Faster R-CNN: Towards Accurate Few-Shot Object Detection with Attentive Feature AlignmentCode1
How Sensitive are Meta-Learners to Dataset Imbalance?Code1
SiT: Self-supervised vIsion TransformerCode1
ORBIT: A Real-World Few-Shot Dataset for Teachable Object RecognitionCode1
FAPIS: A Few-shot Anchor-free Part-based Instance SegmenterCode1
Pre-training strategies and datasets for facial representation learningCode1
von Mises-Fisher Loss: An Exploration of Embedding Geometries for Supervised LearningCode1
MetaNODE: Prototype Optimization as a Neural ODE for Few-Shot LearningCode1
Orthogonal Projection LossCode1
Learning Dynamic Alignment via Meta-filter for Few-shot LearningCode1
Detecting Hate Speech with GPT-3Code1
Improving and Simplifying Pattern Exploiting TrainingCode1
Prototypical Representation Learning for Relation ExtractionCode1
Inductive Relation Prediction by BERTCode1
FSCE: Few-Shot Object Detection via Contrastive Proposal EncodingCode1
Few-shot Open-set Recognition by Transformation ConsistencyCode1
Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot LearningCode1
Image Augmentation for Multitask Few-Shot Learning: Agricultural Domain Use-CaseCode1
Dual-Awareness Attention for Few-Shot Object DetectionCode1
MetaDelta: A Meta-Learning System for Few-shot Image ClassificationCode1
Few-shot Network Anomaly Detection via Cross-network Meta-learningCode1
On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-LearningCode1
Calibrate Before Use: Improving Few-Shot Performance of Language ModelsCode1
GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental LearningCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1gpt-4-0125-previewAccuracy61.91Unverified
2gpt-4-0125-previewAccuracy52.49Unverified
3gpt-3.5-turboAccuracy41.48Unverified
4gpt-3.5-turboAccuracy37.06Unverified
5johnsnowlabs/JSL-MedMNX-7BAccuracy25.63Unverified
6yikuan8/Clinical-LongformerAccuracy25.55Unverified
7BioMistral/BioMistral-7B-DAREAccuracy25.06Unverified
8yikuan8/Clinical-LongformerAccuracy25.04Unverified
9PharMolix/BioMedGPT-LM-7BAccuracy24.92Unverified
10PharMolix/BioMedGPT-LM-7BAccuracy24.75Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean67.27Unverified
2SaSPA + CAL4-shot Accuracy48.3Unverified
3Real-Guidance + CAL4-shot Accuracy41.5Unverified
4CAL4-shot Accuracy40.9Unverified
#ModelMetricClaimedVerifiedStatus
1SaSPA + CALHarmonic mean52.2Unverified
2CALHarmonic mean35.2Unverified
3Variational Prompt TuningHarmonic mean34.69Unverified
4Real-Guidance + CALHarmonic mean34.5Unverified
#ModelMetricClaimedVerifiedStatus
1BGNNAccuracy92.7Unverified
2TIM-GDAccuracy87.4Unverified
3UNEM-GaussianAccuracy66.4Unverified
#ModelMetricClaimedVerifiedStatus
1EASY (transductive)Accuracy82.75Unverified
2HCTransformers5 way 1~2 shot74.74Unverified
3HyperShotAccuracy53.18Unverified
#ModelMetricClaimedVerifiedStatus
1SaSPA + CAL4-shot Accuracy66.7Unverified
2Real-Guidance + CAL4-shot Accuracy44.3Unverified
3CAL4-shot Accuracy42.2Unverified
#ModelMetricClaimedVerifiedStatus
1HCTransformersAcc74.74Unverified
2DPGNAcc67.6Unverified
#ModelMetricClaimedVerifiedStatus
1MetaGen Blended RAG (zero-shot)Accuracy77.9Unverified
2CoT-T5-11B (1024 Shot)Accuracy73.42Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean96.44Unverified
#ModelMetricClaimedVerifiedStatus
1CoT-T5-11B (1024 Shot)Accuracy68.3Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean77.71Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean81.12Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean91.57Unverified
#ModelMetricClaimedVerifiedStatus
1CovidExpertAUC-ROC1Unverified
#ModelMetricClaimedVerifiedStatus
1CoT-T5-11B (1024 Shot)Accuracy78.02Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy65.7Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy73.2Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean96.82Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean73.07Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean78.51Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy52.3Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean79Unverified