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 401450 of 2964 papers

TitleStatusHype
animal2vec and MeerKAT: A self-supervised transformer for rare-event raw audio input and a large-scale reference dataset for bioacousticsCode1
Calibrate Before Use: Improving Few-Shot Performance of Language ModelsCode1
Adversarial Feature Augmentation for Cross-domain Few-shot ClassificationCode1
EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter OptimizationCode1
Expanding Event Modality Applications through a Robust CLIP-Based EncoderCode1
CoNeRF: Controllable Neural Radiance FieldsCode1
EPCL: Frozen CLIP Transformer is An Efficient Point Cloud EncoderCode1
Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot LearningCode1
Exploring Efficient Few-shot Adaptation for Vision TransformersCode1
Extending Context Window of Large Language Models via Semantic CompressionCode1
Can Explanations Be Useful for Calibrating Black Box Models?Code1
Anomaly Detection-Inspired Few-Shot Medical Image Segmentation Through Self-Supervision With SupervoxelsCode1
Anomaly Detection of Defect using Energy of Point Pattern Features within Random Finite Set FrameworkCode1
Leveraging Hierarchical Structures for Few-Shot Musical Instrument RecognitionCode1
ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft PromptsCode1
FAITH: Few-Shot Graph Classification with Hierarchical Task GraphsCode1
Feature Generation for Long-tail ClassificationCode1
FAPIS: A Few-shot Anchor-free Part-based Instance SegmenterCode1
CDFSL-V: Cross-Domain Few-Shot Learning for VideosCode1
Consistency-guided Prompt Learning for Vision-Language ModelsCode1
From Examples to Rules: Neural Guided Rule Synthesis for Information ExtractionCode1
LiST: Lite Prompted Self-training Makes Parameter-Efficient Few-shot LearnersCode1
FSCE: Few-Shot Object Detection via Contrastive Proposal EncodingCode1
Generalized and Incremental Few-Shot Learning by Explicit Learning and Calibration without ForgettingCode1
Compressing Lengthy Context With UltraGistCode1
Concept Learners for Few-Shot LearningCode1
Overcoming challenges in leveraging GANs for few-shot data augmentationCode1
Chameleon: A MatMul-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential DataCode1
Channel Importance Matters in Few-Shot Image ClassificationCode1
Federated Few-shot LearningCode1
FlipDA: Effective and Robust Data Augmentation for Few-Shot LearningCode1
Charting the Right Manifold: Manifold Mixup for Few-shot LearningCode1
FewCLUE: A Chinese Few-shot Learning Evaluation BenchmarkCode1
Few-Shot and Continual Learning with Attentive Independent MechanismsCode1
Flatten Long-Range Loss Landscapes for Cross-Domain Few-Shot LearningCode1
Few-shot Adaptation Works with UnpredicTable DataCode1
MAML is a Noisy Contrastive Learner in ClassificationCode1
Many-Class Few-Shot Learning on Multi-Granularity Class HierarchyCode1
FLEX: Unifying Evaluation for Few-Shot NLPCode1
FOCUS: Knowledge-enhanced Adaptive Visual Compression for Few-shot Whole Slide Image ClassificationCode1
Few-shot bioacoustic event detection at the DCASE 2023 challengeCode1
Few-shot Classification via Adaptive AttentionCode1
Finetune like you pretrain: Improved finetuning of zero-shot vision modelsCode1
Match Them Up: Visually Explainable Few-shot Image ClassificationCode1
Few-Shot Bot: Prompt-Based Learning for Dialogue SystemsCode1
Class-Incremental Domain Adaptation with Smoothing and Calibration for Surgical Report GenerationCode1
A Rationale-Centric Framework for Human-in-the-loop Machine LearningCode1
Few-Shot Diffusion ModelsCode1
A Few-shot Learning Approach for Historical Ciphered Manuscript RecognitionCode1
Few-Shot Classification with Feature Map Reconstruction NetworksCode1
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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