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 12511300 of 2964 papers

TitleStatusHype
A Weak Supervision Approach for Few-Shot Aspect Based Sentiment0
iFuzzyTL: Interpretable Fuzzy Transfer Learning for SSVEP BCI System0
Impossible Triangle: What's Next for Pre-trained Language Models?0
Improving Instruct Models for Free: A Study on Partial Adaptation0
Dataset Bias in Few-shot Image Recognition0
A Weakly Supervised Segmentation Network Embedding Cross-scale Attention Guidance and Noise-sensitive Constraint for Detecting Tertiary Lymphoid Structures of Pancreatic Tumors0
Alpha MAML: Adaptive Model-Agnostic Meta-Learning0
Data-Efficient Goal-Oriented Conversation with Dialogue Knowledge Transfer Networks0
HyperTransformer: Attention-Based CNN Model Generation from Few Samples0
VLM Agents Generate Their Own Memories: Distilling Experience into Embodied Programs of Thought0
Generalization Bounds for Few-Shot Transfer Learning with Pretrained Classifiers0
AutoWS: Automated Weak Supervision Framework for Text Classification0
Hyperspectral Imaging-Based Grain Quality Assessment With Limited Labelled Data0
A Comprehensive Evaluation of Large Language Models on Mental Illnesses in Arabic Context0
GenCo: An Auxiliary Generator from Contrastive Learning for Enhanced Few-Shot Learning in Remote Sensing0
GeMQuAD : Generating Multilingual Question Answering Datasets from Large Language Models using Few Shot Learning0
GDC- Generalized Distribution Calibration for Few-Shot Learning0
Auto-view contrastive learning for few-shot image recognition0
Hyperspherical embedding for novel class classification0
I Can Find You in Seconds! Leveraging Large Language Models for Code Authorship Attribution0
Generalization of Fitness Exercise Recognition from Doppler Measurements by Domain-adaption and Few-Shot Learning0
GCCN: Global Context Convolutional Network0
Generalized Cross-domain Multi-label Few-shot Learning for Chest X-rays0
A Versatile Framework for Analyzing Galaxy Image Data by Implanting Human-in-the-loop on a Large Vision Model0
DASECount: Domain-Agnostic Sample-Efficient Wireless Indoor Crowd Counting via Few-shot Learning0
Autosen: improving automatic wifi human sensing through cross-modal autoencoder0
Generalized Reinforcement Meta Learning for Few-Shot Optimization0
Generalized Sampling Method for Few Shot Learning0
Gaussian Process Conditional Density Estimation0
Adaptive Prompt Tuning: Vision Guided Prompt Tuning with Cross-Attention for Fine-Grained Few-Shot Learning0
Hyperbolic Uncertainty-Aware Few-Shot Incremental Point Cloud Segmentation0
f-VAEGAN-D2: A Feature Generating Framework for Any-Shot Learning0
Fuzzy Simplicial Networks: A Topology-Inspired Model to Improve Task Generalization in Few-shot Learning0
DA-FDFtNet: Dual Attention Fake Detection Fine-tuning Network to Detect Various AI-Generated Fake Images0
ALMA: Alignment with Minimal Annotation0
Hyperbolic Dual Feature Augmentation for Open-Environment0
Hyperbolic vs Euclidean Embeddings in Few-Shot Learning: Two Sides of the Same Coin0
FungiTastic: A multi-modal dataset and benchmark for image categorization0
Generating Synthetic Datasets for Few-shot Prompt Tuning0
Function-words Enhanced Attention Networks for Few-Shot Inverse Relation Classification0
Decomposed Prototype Learning for Few-Shot Scene Graph Generation0
Generative Adversarial Networks Based on Transformer Encoder and Convolution Block for Hyperspectral Image Classification0
Generative AI Is Not Ready for Clinical Use in Patient Education for Lower Back Pain Patients, Even With Retrieval-Augmented Generation0
Generative Pre-trained Autoregressive Diffusion Transformer0
CVOCSemRPL: Class-Variance Optimized Clustering, Semantic Information Injection and Restricted Pseudo Labeling based Improved Semi-Supervised Few-Shot Learning0
Function Contrastive Learning of Transferable Meta-Representations0
Function Contrastive Learning of Transferable Representations0
Bad Form: Comparing Context-Based and Form-Based Few-Shot Learning in Distributional Semantic Models0
Automatic Validation of Textual Attribute Values in E-commerce Catalog by Learning with Limited Labeled Data0
Fully Fine-tuned CLIP Models are Efficient Few-Shot Learners0
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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