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 751800 of 2964 papers

TitleStatusHype
Flexibly Scaling Large Language Models Contexts Through Extensible Tokenization0
Harnessing Large Language Models Over Transformer Models for Detecting Bengali Depressive Social Media Text: A Comprehensive StudyCode0
EHRAgent: Code Empowers Large Language Models for Few-shot Complex Tabular Reasoning on Electronic Health RecordsCode2
xCoT: Cross-lingual Instruction Tuning for Cross-lingual Chain-of-Thought Reasoning0
Hierarchical Knowledge Distillation on Text Graph for Data-limited Attribute Inference0
Less is More: A Closer Look at Semantic-based Few-Shot Learning0
Derm-T2IM: Harnessing Synthetic Skin Lesion Data via Stable Diffusion Models for Enhanced Skin Disease Classification using ViT and CNN0
Make Prompts Adaptable: Bayesian Modeling for Vision-Language Prompt Learning with Data-Dependent PriorCode1
Deep learning based detection of collateral circulation in coronary angiographies0
Autosen: improving automatic wifi human sensing through cross-modal autoencoder0
Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time SeriesCode4
Long Context Compression with Activation Beacon0
Few-Shot Causal Representation Learning for Out-of-Distribution Generalization on Heterogeneous Graphs0
Few-Shot Object Detection with Foundation Models0
Adversarially Robust Few-shot Learning via Parameter Co-distillation of Similarity and Class Concept Learners0
Beyond Textual Constraints: Learning Novel Diffusion Conditions with Fewer ExamplesCode0
DeIL: Direct-and-Inverse CLIP for Open-World Few-Shot LearningCode1
Does Few-shot Learning Suffer from Backdoor Attacks?0
Self-supervised Pretraining for Decision Foundation Model: Formulation, Pipeline and Challenges0
Few-shot learning for automated content analysis: Efficient coding of arguments and claims in the debate on arms deliveries to Ukraine0
A Prompt Learning Framework for Source Code SummarizationCode1
MetaScript: Few-Shot Handwritten Chinese Content Generation via Generative Adversarial NetworksCode1
Self-Supervised Learning for Few-Shot Bird Sound ClassificationCode1
Prototype-Based Approach for One-Shot Segmentation of Brain Tumors using Few-Shot Learning0
Learning Human-like Representations to Enable Learning Human Values0
Meta-Learning with Versatile Loss Geometries for Fast Adaptation Using Mirror DescentCode0
ProS: Prompting-to-simulate Generalized knowledge for Universal Cross-Domain RetrievalCode1
Advancing Image Retrieval with Few-Shot Learning and Relevance FeedbackCode0
LaViP:Language-Grounded Visual Prompts0
Leveraging Normalization Layer in Adapters With Progressive Learning and Adaptive Distillation for Cross-Domain Few-Shot LearningCode0
Few-Shot Learning from Augmented Label-Uncertain Queries in Bongard-HOI0
Grammatical information in BERT sentence embeddings as two-dimensional arrays0
Extending Context Window of Large Language Models via Semantic CompressionCode1
Continual Adversarial DefenseCode0
Metacognition-Enhanced Few-Shot Prompting With Positive Reinforcement0
Fewer is More: Boosting LLM Reasoning with Reinforced Context Pruning0
A Comparative Analysis of Fine-Tuned LLMs and Few-Shot Learning of LLMs for Financial Sentiment Analysis0
Beyond English: Evaluating LLMs for Arabic Grammatical Error Correction0
Prompt Engineering-assisted Malware Dynamic Analysis Using GPT-4Code1
Self-supervised Adaptive Pre-training of Multilingual Speech Models for Language and Dialect Identification0
SGLang: Efficient Execution of Structured Language Model ProgramsCode6
TransMed: Large Language Models Enhance Vision Transformer for Biomedical Image Classification0
Few-Shot Class-Incremental Learning via Training-Free Prototype CalibrationCode1
Adaptive Weighted Co-Learning for Cross-Domain Few-Shot Learning0
Parameter-Efficient Transfer Learning of Audio Spectrogram TransformersCode1
Assertion Enhanced Few-Shot Learning: Instructive Technique for Large Language Models to Generate Educational Explanations0
Diversified in-domain synthesis with efficient fine-tuning for few-shot classificationCode1
Near-real-time Earthquake-induced Fatality Estimation using Crowdsourced Data and Large-Language ModelsCode0
Evaluating General Purpose Vision Foundation Models for Medical Image Analysis: An Experimental Study of DINOv2 on Radiology BenchmarksCode1
D^2ST-Adapter: Disentangled-and-Deformable Spatio-Temporal Adapter for Few-shot Action RecognitionCode1
Show:102550
← PrevPage 16 of 60Next →

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