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 15011550 of 2964 papers

TitleStatusHype
Fine-grained Few-shot Recognition by Deep Object Parsing0
Instance Selection Mechanisms for Human-in-the-Loop Systems in Few-Shot LearningCode0
Convolutional Bypasses Are Better Vision Transformer AdaptersCode1
Pseudo-Labeling Based Practical Semi-Supervised Meta-Training for Few-Shot LearningCode0
Proceedings of the ICML 2022 Expressive Vocalizations Workshop and Competition: Recognizing, Generating, and Personalizing Vocal Bursts0
A Reinforcement Learning-based Offensive semantics Censorship System for Chatbots0
ViQuAE, a Dataset for Knowledge-based Visual Question Answering about Named EntitiesCode1
Continual Few-Shot Learning with Adversarial Class Storage0
Generating Pseudo-labels Adaptively for Few-shot Model-Agnostic Meta-Learning0
Few 'Zero Level Set'-Shot Learning of Shape Signed Distance Functions in Feature SpaceCode0
Few-shot training LLMs for project-specific code-summarization0
On the Subspace Structure of Gradient-Based Meta-Learning0
Few-Shot Scene Classification of Optical Remote Sensing Images Leveraging Calibrated Pretext TasksCode2
FewSOL: A Dataset for Few-Shot Object Learning in Robotic EnvironmentsCode0
DCT-Net: Domain-Calibrated Translation for Portrait StylizationCode2
CAM/CAD Point Cloud Part Segmentation via Few-Shot Learning0
Dynamic Sub-Cluster-Aware Network for Few-Shot Skin Disease ClassificationCode0
Strategies to Improve Few-shot Learning for Intent Classification and Slot-Filling0
Interactive Symbol Grounding with Complex Referential ExpressionsCode0
CS/NLP at SemEval-2022 Task 4: Effective Data Augmentation Methods for Patronizing Language Detection and Multi-label Classification with RoBERTa and GPT30
Few-shot fine-tuning SOTA summarization models for medical dialogues0
AISFG: Abundant Information Slot Filling Generator0
RGL: A Simple yet Effective Relation Graph Augmented Prompt-based Tuning Approach for Few-Shot Learning0
LEA: Meta Knowledge-Driven Self-Attentive Document Embedding for Few-Shot Text Classification0
Few-shot Learning for Sumerian Named Entity Recognition0
Graph Information Aggregation Cross-Domain Few-Shot Learning for Hyperspectral Image ClassificationCode1
Domain Agnostic Few-shot Learning for Speaker Verification0
Few-Shot Cross-Lingual TTS Using Transferable Phoneme Embedding0
Understanding Benign Overfitting in Gradient-Based Meta Learning0
Prompting Decision Transformer for Few-Shot Policy Generalization0
Few-Shot Stance Detection via Target-Aware Prompt Distillation0
Task-Adaptive Few-shot Node ClassificationCode1
Single Morphing Attack Detection using Siamese Network and Few-shot Learning0
Low Resource Pipeline for Spoken Language Understanding via Weak Supervision0
Few-Max: Few-Shot Domain Adaptation for Unsupervised Contrastive Representation LearningCode0
Contextual Squeeze-and-Excitation for Efficient Few-Shot Image ClassificationCode1
C^*-algebra Net: A New Approach Generalizing Neural Network Parameters to C^*-algebra0
Model-Agnostic Few-Shot Open-Set RecognitionCode1
EEML: Ensemble Embedded Meta-learning0
Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement LearningCode2
FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image ClassificationCode1
Lifelong Wandering: A realistic few-shot online continual learning setting0
Channel Importance Matters in Few-Shot Image ClassificationCode1
Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification0
Rethinking Generalization in Few-Shot ClassificationCode1
NatGen: Generative pre-training by "Naturalizing" source codeCode1
Language Models are General-Purpose Interfaces0
From Human Days to Machine Seconds: Automatically Answering and Generating Machine Learning Final Exams0
Real-time Hyper-Dimensional Reconfiguration at the Edge using Hardware Accelerators0
Neural Prompt SearchCode2
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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