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 651700 of 2964 papers

TitleStatusHype
DeepEMD: Differentiable Earth Mover's Distance for Few-Shot LearningCode1
Few-Shot Semantic Parsing for New PredicatesCode1
Few-Shot Single-View 3-D Object Reconstruction with Compositional PriorsCode1
Bayesian Meta-Learning for the Few-Shot Setting via Deep KernelsCode1
Learning to Learn to Disambiguate: Meta-Learning for Few-Shot Word Sense DisambiguationCode1
Few-Shot Unsupervised Continual Learning through Meta-ExamplesCode1
Domain-Adaptive Few-Shot LearningCode1
Few-Shot Video Object DetectionCode1
Concept Learners for Few-Shot LearningCode1
FewVS: A Vision-Semantics Integration Framework for Few-Shot Image ClassificationCode1
Deep Metric Learning for Open World Semantic SegmentationCode1
BECLR: Batch Enhanced Contrastive Few-Shot LearningCode1
Efficient Few-shot Learning for Multi-label Classification of Scientific Documents with Many ClassesCode1
Why So Gullible? Enhancing the Robustness of Retrieval-Augmented Models against Counterfactual NoiseCode1
FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image ClassificationCode1
Finetune like you pretrain: Improved finetuning of zero-shot vision modelsCode1
Defining Benchmarks for Continual Few-Shot LearningCode1
Deformation-Recovery Diffusion Model (DRDM): Instance Deformation for Image Manipulation and SynthesisCode1
FlamePINN-1D: Physics-informed neural networks to solve forward and inverse problems of 1D laminar flamesCode1
DeIL: Direct-and-Inverse CLIP for Open-World Few-Shot LearningCode1
AdaptKeyBERT: An Attention-Based approach towards Few-Shot & Zero-Shot Domain Adaptation of KeyBERTCode1
FLEX: Unifying Evaluation for Few-Shot NLPCode1
Diffusion Mechanism in Residual Neural Network: Theory and ApplicationsCode1
POODLE: Improving Few-shot Learning via Penalizing Out-of-Distribution SamplesCode1
Disentangle and Remerge: Interventional Knowledge Distillation for Few-Shot Object Detection from A Conditional Causal PerspectiveCode1
FOCUS: Knowledge-enhanced Adaptive Visual Compression for Few-shot Whole Slide Image ClassificationCode1
Better Generalized Few-Shot Learning Even Without Base DataCode1
DenoiSeg: Joint Denoising and SegmentationCode1
From LSAT: The Progress and Challenges of Complex ReasoningCode1
EPCL: Frozen CLIP Transformer is An Efficient Point Cloud EncoderCode1
Generalising via Meta-Examples for Continual Learning in the WildCode1
DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuningCode1
Few-shot Network Anomaly Detection via Cross-network Meta-learningCode1
Learning to Reason in Round-based Games: Multi-task Sequence Generation for Purchasing Decision Making in First-person ShootersCode1
Prompt Engineering-assisted Malware Dynamic Analysis Using GPT-4Code1
Design of a Graphical User Interface for Few-Shot Machine Learning Classification of Electron Microscopy DataCode1
DETA: Denoised Task Adaptation for Few-Shot LearningCode1
Generalization-Enhanced Few-Shot Object Detection in Remote SensingCode1
Linear algebra with transformersCode1
Detecting Hate Speech with GPT-3Code1
ProS: Prompting-to-simulate Generalized knowledge for Universal Cross-Domain RetrievalCode1
DETReg: Unsupervised Pretraining with Region Priors for Object DetectionCode1
ProtoDiff: Learning to Learn Prototypical Networks by Task-Guided DiffusionCode1
Diagnosing Infeasible Optimization Problems Using Large Language ModelsCode1
Making Pre-trained Language Models Better Few-shot LearnersCode1
Dialogue for Prompting: a Policy-Gradient-Based Discrete Prompt Generation for Few-shot LearningCode1
Meta-Learning with Implicit GradientsCode1
Generative Pretrained Hierarchical Transformer for Time Series ForecastingCode1
ORBIT: A Real-World Few-Shot Dataset for Teachable Object RecognitionCode1
See, Think, Confirm: Interactive Prompting Between Vision and Language Models for Knowledge-based Visual ReasoningCode1
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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