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 23512400 of 2964 papers

TitleStatusHype
SigNet: A Novel Deep Learning Framework for Radio Signal Classification0
Malicious Requests Detection with Improved Bidirectional Long Short-term Memory Neural Networks0
Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language InferenceCode1
Few-shot Decoding of Brain Activation MapsCode1
Restoring Negative Information in Few-Shot Object DetectionCode1
Task-Adaptive Feature Transformer for Few-Shot Segmentation0
Few-shot Image Recognition with Manifolds0
Learning to Learn Variational Semantic MemoryCode0
Directed Variational Cross-encoder Network for Few-shot Multi-image Co-segmentation0
On the Utility of Active Instance Selection for Few-Shot Learning0
Training Data Generating Networks: Shape Reconstruction via Bi-level Optimization0
ALPaCA vs. GP-based Prior Learning: A Comparison between two Bayesian Meta-Learning AlgorithmsCode0
Self-training for Few-shot Transfer Across Extreme Task DifferencesCode1
Theoretical bounds on estimation error for meta-learning0
Function Contrastive Learning of Transferable Meta-Representations0
Few-shot Action Recognition with Implicit Temporal Alignment and Pair Similarity Optimization0
The Tatoeba Translation Challenge -- Realistic Data Sets for Low Resource and Multilingual MTCode1
Cross-Domain Few-Shot Learning by Representation FusionCode1
Structural Supervision Improves Few-Shot Learning and Syntactic Generalization in Neural Language Models0
Few-shot Learning for Multi-label Intent Detection0
Addressing the Real-world Class Imbalance Problem in Dermatology0
Few-shot Learning for Spatial Regression0
Uncertainty-Aware Few-Shot Image Classification0
Learning Clusterable Visual Features for Zero-Shot Recognition0
Dynamic Semantic Matching and Aggregation Network for Few-shot Intent DetectionCode0
Shot in the Dark: Few-Shot Learning with No Base-Class Labels0
Representation learning from videos in-the-wild: An object-centric approach0
Improving Few-Shot Learning through Multi-task Representation Learning TheoryCode0
Self-training Improves Pre-training for Natural Language UnderstandingCode1
Unknown Presentation Attack Detection against Rational Attackers0
Data-Efficient Pretraining via Contrastive Self-Supervision0
Cross-Lingual Transfer Learning for Complex Word Identification0
IsOBS: An Information System for Oracle Bone Script0
Few-shot Learning for Time-series Forecasting0
MetaMix: Improved Meta-Learning with Interpolation-based Consistency Regularization0
Message Passing Neural Processes0
Learning to Generate Image Source-Agnostic Universal Adversarial Perturbations0
Self-Supervised Few-Shot Learning on Point CloudsCode1
Function Contrastive Learning of Transferable Representations0
Self-supervised Contrastive Zero to Few-shot Learning from Small, Long-tailed Text data0
Non-greedy Gradient-based Hyperparameter Optimization Over Long Horizons0
Improving Few-Shot Visual Classification with Unlabelled Examples0
Putting Theory to Work: From Learning Bounds to Meta-Learning Algorithms0
Sense and Learn: Self-Supervision for Omnipresent Sensors0
Interventional Few-Shot LearningCode1
A Primal-Dual Subgradient Approachfor Fair Meta LearningCode0
A Few-shot Learning Approach for Historical Ciphered Manuscript RecognitionCode1
Fuzzy Simplicial Networks: A Topology-Inspired Model to Improve Task Generalization in Few-shot Learning0
PennSyn2Real: Training Object Recognition Models without Human Labeling0
Vector Projection Network for Few-shot Slot Tagging in Natural Language UnderstandingCode1
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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