SOTAVerified

Transfer Learning

Transfer Learning is a machine learning technique where a model trained on one task is re-purposed and fine-tuned for a related, but different task. The idea behind transfer learning is to leverage the knowledge learned from a pre-trained model to solve a new, but related problem. This can be useful in situations where there is limited data available to train a new model from scratch, or when the new task is similar enough to the original task that the pre-trained model can be adapted to the new problem with only minor modifications.

( Image credit: Subodh Malgonde )

Papers

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Showing 751–775 of 10307 papers

TitleStatusHype
Beyond Self-Supervision: A Simple Yet Effective Network Distillation Alternative to Improve BackbonesCode1
An Evaluation of Self-Supervised Pre-Training for Skin-Lesion AnalysisCode1
FastSal: a Computationally Efficient Network for Visual Saliency PredictionCode1
An Evolutionary Multitasking Algorithm with Multiple Filtering for High-Dimensional Feature SelectionCode1
Bilevel Continual LearningCode1
Feature-based Federated Transfer Learning: Communication Efficiency, Robustness and PrivacyCode1
Benchmarking Detection Transfer Learning with Vision TransformersCode1
FedClassAvg: Local Representation Learning for Personalized Federated Learning on Heterogeneous Neural NetworksCode1
KNOT: Knowledge Distillation using Optimal Transport for Solving NLP TasksCode1
A New Knowledge Distillation Network for Incremental Few-Shot Surface Defect DetectionCode1
Bert4XMR: Cross-Market Recommendation with Bidirectional Encoder Representations from TransformerCode1
FedMD: Heterogenous Federated Learning via Model DistillationCode1
Few-Sample Named Entity Recognition for Security Vulnerability Reports by Fine-Tuning Pre-Trained Language ModelsCode1
Few-shot Image Classification: Just Use a Library of Pre-trained Feature Extractors and a Simple ClassifierCode1
Few-Shot Keyword Spotting in Any LanguageCode1
Blindly Assess Quality of In-the-Wild Videos via Quality-aware Pre-training and Motion PerceptionCode1
Boosting Memory Efficiency in Transfer Learning for High-Resolution Medical Image ClassificationCode1
Fine-tuning Graph Neural Networks by Preserving Graph Generative PatternsCode1
Bridging the User-side Knowledge Gap in Knowledge-aware Recommendations with Large Language ModelsCode1
Fine-Tuning Self-Supervised Learning Models for End-to-End Pronunciation ScoringCode1
FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image ClassificationCode1
Fix the Noise: Disentangling Source Feature for Transfer Learning of StyleGANCode1
BadMerging: Backdoor Attacks Against Model MergingCode1
Flight Contrail Segmentation via Augmented Transfer Learning with Novel SR Loss Function in Hough SpaceCode1
Adapting Pre-trained Vision Transformers from 2D to 3D through Weight Inflation Improves Medical Image SegmentationCode1
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All datasetsOffice-Home100 sleep nights of 8 caregiversBanglaLekha Isolated DatasetCOCO70KITTI Object Tracking Evaluation 2012Retinal Fundus MultiDisease Image Dataset (RFMiD)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1APCLIPAccuracy84.2—Unverified
2DFA-ENTAccuracy69.2—Unverified
3DFA-SAFNAccuracy69.1—Unverified
4EasyTLAccuracy63.3—Unverified
5MEDAAccuracy60.3—Unverified
#ModelMetricClaimedVerifiedStatus
1CNN10-20% Mask PSNR3.23—Unverified
#ModelMetricClaimedVerifiedStatus
1Chatterjee, Dutta et al.[1]Accuracy96.12—Unverified
#ModelMetricClaimedVerifiedStatus
1Co-TuningAccuracy85.65—Unverified
#ModelMetricClaimedVerifiedStatus
1Physical AccessEER5.74—Unverified
#ModelMetricClaimedVerifiedStatus
1riadd.aucmediAUROC0.95—Unverified