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 26012650 of 2964 papers

TitleStatusHype
Few-Shot Learning on Graphs via Super-Classes based on Graph Spectral MeasuresCode1
Evolving Losses for Unsupervised Video Representation Learning0
An Open-set Recognition and Few-Shot Learning Dataset for Audio Event Classification in Domestic EnvironmentsCode0
The Sample Complexity of Meta Sparse Regression0
Modelling Latent Skills for Multitask Language Generation0
Few-Shot Learning via Learning the Representation, Provably0
Few-shot acoustic event detection via meta-learning0
Structured Prediction for Conditional Meta-LearningCode0
Few-Shot Few-Shot Learning and the role of Spatial Attention0
Exploiting the Matching Information in the Support Set for Few Shot Event Classification0
Meta-Learning across Meta-Tasks for Few-Shot Learning0
Task Augmentation by Rotating for Meta-LearningCode0
Conditional Deep Gaussian Processes: multi-fidelity kernel learningCode0
Few-Shot Learning as Domain Adaptation: Algorithm and Analysis0
Revisiting Meta-Learning as Supervised Learning0
Asymmetric Distribution Measure for Few-shot Learning0
Weakly Supervised Few-shot Object Segmentation using Co-Attention with Visual and Semantic Embeddings0
Continual Local Replacement for Few-shot Learning0
Evaluating Weakly Supervised Object Localization Methods RightCode1
Few-shot Action Recognition with Permutation-invariant AttentionCode0
Rethinking Class Relations: Absolute-relative Supervised and Unsupervised Few-shot LearningCode1
Fine-grained Image-to-Image Transformation towards Visual Recognition0
Intelligence, physics and information -- the tradeoff between accuracy and simplicity in machine learningCode0
Improving Few-shot Learning by Spatially-aware Matching and CrossTransformer0
FDFtNet: Facing Off Fake Images using Fake Detection Fine-tuning NetworkCode1
Diversity Transfer Network for Few-Shot LearningCode0
FLAT: Few-Shot Learning via Autoencoding Transformation Regularizers0
Variational Metric Scaling for Metric-Based Meta-LearningCode0
Big Transfer (BiT): General Visual Representation LearningCode2
Measuring Dataset GranularityCode0
Unsupervised Few-shot Learning via Self-supervised Training0
Dependable Neural Networks for Safety Critical Tasks0
Identity Document to Selfie Face Matching Across Adolescence0
TransMatch: A Transfer-Learning Scheme for Semi-Supervised Few-Shot Learning0
A Broader Study of Cross-Domain Few-Shot LearningCode1
Towards Contextual Learning in Few-shot Object Classification0
Meta-Learning Initializations for Image SegmentationCode0
CLOSURE: Assessing Systematic Generalization of CLEVR ModelsCode0
A Two-Stage Approach to Few-Shot Learning for Image Recognition0
Improved Few-Shot Visual Classification0
Revisiting Few-Shot Learning for Facial Expression Recognition0
MetAdapt: Meta-Learned Task-Adaptive Architecture for Few-Shot Classification0
SMILe: Scalable Meta Inverse Reinforcement Learning through Context-Conditional PoliciesCode0
Learning to Learn By Self-CritiqueCode0
Efficient Meta Learning via Minibatch Proximal Update0
Unlocking the Full Potential of Small Data with Diverse SupervisionCode0
VIABLE: Fast Adaptation via Backpropagating Learned Loss0
A Unified Framework for Lifelong Learning in Deep Neural Networks0
Learning Multi-level Weight-centric Features for Few-shot Learning0
Meta-Learning of Neural Architectures for Few-Shot LearningCode1
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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