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 2651–2675 of 10307 papers

TitleStatusHype
Cross Dataset Analysis and Network Architecture Repair for Autonomous Car Lane Detection—0
A study on Deep Convolutional Neural Networks, Transfer Learning and Ensemble Model for Breast Cancer Detection—0
Robust Real-time Segmentation of Bio-Morphological Features in Human Cherenkov Imaging during Radiotherapy via Deep Learning—0
Sample-Efficient Bayesian Optimization with Transfer Learning for Heterogeneous Search SpacesCode0
Multilingual Dyadic Interaction Corpus NoXi+J: Toward Understanding Asian-European Non-verbal Cultural Characteristics and their Influences on Engagement—0
Look One and More: Distilling Hybrid Order Relational Knowledge for Cross-Resolution Image Recognition—0
Federated Transfer Learning Based Cooperative Wideband Spectrum Sensing with Model Pruning—0
Learning with Shared Representations: Statistical Rates and Efficient Algorithms—0
Urban traffic analysis and forecasting through shared Koopman eigenmodes—0
Advancing Automated Knowledge Transfer in Evolutionary Multitasking via Large Language Models—0
A Unified Framework for Cross-Domain Recommendation—0
Threat Classification on Deployed Optical Networks Using MIMO Digital Fiber Sensing, Wavelets, and Machine Learning—0
Deep Clustering of Remote Sensing Scenes through Heterogeneous Transfer Learning—0
Shuffle Vision Transformer: Lightweight, Fast and Efficient Recognition of Driver Facial Expression—0
FedPCL-CDR: A Federated Prototype-based Contrastive Learning Framework for Privacy-Preserving Cross-domain RecommendationCode0
Non-stationary and Sparsely-correlated Multi-output Gaussian Process with Spike-and-Slab Prior—0
Regularized Multi-output Gaussian Convolution Process with Domain Adaptation—0
Low-Resolution Object Recognition with Cross-Resolution Relational Contrastive Distillation—0
Learning Privacy-Preserving Student Networks via Discriminative-Generative Distillation—0
iConFormer: Dynamic Parameter-Efficient Tuning with Input-Conditioned Adaptation—0
Temporal Order Preserved Optimal Transport-based Cross-modal Knowledge Transfer Learning for ASR—0
Surveying You Only Look Once (YOLO) Multispectral Object Detection Advancements, Applications And Challenges—0
When Does Visual Prompting Outperform Linear Probing for Vision-Language Models? A Likelihood PerspectiveCode0
Dynamic Guidance Adversarial Distillation with Enhanced Teacher KnowledgeCode0
Adaptive Explicit Knowledge Transfer for Knowledge Distillation—0
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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