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 14511500 of 2964 papers

TitleStatusHype
Procedural Text Mining with Large Language ModelsCode0
PrototypeFormer: Learning to Explore Prototype Relationships for Few-shot Image Classification0
Retrieval meets Long Context Large Language Models0
SHOT: Suppressing the Hessian along the Optimization Trajectory for Gradient-Based Meta-LearningCode0
TRAM: Benchmarking Temporal Reasoning for Large Language Models0
RA-DIT: Retrieval-Augmented Dual Instruction Tuning0
PACIA: Parameter-Efficient Adapter for Few-Shot Molecular Property PredictionCode0
SIP: Injecting a Structural Inductive Bias into a Seq2Seq Model by SimulationCode0
Self-Supervised Open-Ended Classification with Small Visual Language Models0
On the Role of Neural Collapse in Meta Learning Models for Few-shot LearningCode0
An evaluation of GPT models for phenotype concept recognition0
Logarithm-transform aided Gaussian Sampling for Few-Shot LearningCode0
Large Language Models in Finance: A Survey0
Multi-unit soft sensing permits few-shot learning0
Robust Internal Representations for Domain Generalization0
Knowledgeable In-Context Tuning: Exploring and Exploiting Factual Knowledge for In-Context Learning0
HAVE-Net: Hallucinated Audio-Visual Embeddings for Few-Shot Classification with Unimodal Cues0
A Systematic Review of Few-Shot Learning in Medical Imaging0
Exploiting Causality Signals in Medical Images: A Pilot Study with Empirical ResultsCode0
MAD: Meta Adversarial Defense BenchmarkCode0
Hyperbolic vs Euclidean Embeddings in Few-Shot Learning: Two Sides of the Same Coin0
Regularized Contrastive Pre-training for Few-shot Bioacoustic Sound DetectionCode0
Using Large Language Model to Solve and Explain Physics Word Problems Approaching Human Level0
Where2Explore: Few-shot Affordance Learning for Unseen Novel Categories of Articulated Objects0
Gpachov at CheckThat! 2023: A Diverse Multi-Approach Ensemble for Subjectivity Detection in News Articles0
Generalized Cross-domain Multi-label Few-shot Learning for Chest X-rays0
Few-Shot Learning of Force-Based Motions From Demonstration Through Pre-training of Haptic Representation0
Text-to-feature diffusion for audio-visual few-shot learningCode0
Support-Set Context Matters for Bongard ProblemsCode0
Dual Adversarial Alignment for Realistic Support-Query Shift Few-shot Learning0
Federated Few-shot Learning for Cough Classification with Edge DevicesCode0
Bias Testing and Mitigation in LLM-based Code Generation0
LoGoPrompt: Synthetic Text Images Can Be Good Visual Prompts for Vision-Language Models0
Few-shot Diagnosis of Chest x-rays Using an Ensemble of Random Discriminative SubspacesCode0
Semi-Supervised SAR ATR Framework with Transductive Auxiliary Segmentation0
MerA: Merging Pretrained Adapters For Few-Shot Learning0
TransPrompt v2: A Transferable Prompting Framework for Cross-task Text Classification0
When hard negative sampling meets supervised contrastive learning0
VesselShot: Few-shot learning for cerebral blood vessel segmentation0
Fair Few-shot Learning with Auxiliary Sets0
Compressor-Based Classification for Atrial Fibrillation Detection0
Large Language Models Vote: Prompting for Rare Disease IdentificationCode0
Cabrita: closing the gap for foreign languages0
Few-shot Anomaly Detection in Text with Deviation Learning0
COCA: Classifier-Oriented Calibration via Textual Prototype for Source-Free Universal Domain AdaptationCode0
Refashioning Emotion Recognition Modelling: The Advent of Generalised Large Models0
Measuring the Effect of Causal Disentanglement on the Adversarial Robustness of Neural Network Models0
UniAP: Towards Universal Animal Perception in Vision via Few-shot Learning0
BioMedGPT: Open Multimodal Generative Pre-trained Transformer for BioMedicine0
CodeCoT: Tackling Code Syntax Errors in CoT Reasoning for Code Generation0
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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