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 2751–2775 of 10307 papers

TitleStatusHype
Distributed Learning in Heterogeneous Environment: federated learning with adaptive aggregation and computation reduction—0
Distribution-Preserving k-Anonymity—0
Anaphoric Zero Pronoun Identification: A Multilingual Approach—0
Deep Reinforcement Learning Based Cross-Layer Design in Terahertz Mesh Backhaul Networks—0
Deep Reinforcement Learning for Day-to-day Dynamic Tolling in Tradable Credit Schemes—0
Deep reinforcement learning for guidewire navigation in coronary artery phantom—0
Deep Reinforcement Learning for Multi-Agent Systems: A Review of Challenges, Solutions and Applications—0
Deep reinforcement learning for optical systems: A case study of mode-locked lasers—0
Deep Reinforcement Learning to Maximize Arterial Usage during Extreme Congestion—0
Semi-Supervised Domain Adaptation with Representation Learning for Semantic Segmentation across Time—0
Deep representation of EEG data from Spatio-Spectral Feature Images—0
Balancing Accuracy and Training Time in Federated Learning for Violence Detection in Surveillance Videos: A Study of Neural Network Architectures—0
Do Better ImageNet Models Transfer Better?—0
Does Robustness on ImageNet Transfer to Downstream Tasks?—0
Deep SIMBAD: Active Landmark-based Self-localization Using Ranking -based Scene Descriptor—0
Deep Learning for EEG Seizure Detection in Preterm Infants—0
AutoTransfer: Subject Transfer Learning with Censored Representations on Biosignals Data—0
Deep Learning for Steganalysis of Diverse Data Types: A review of methods, taxonomy, challenges and future directions—0
Deep Stacking Networks for Low-Resource Chinese Word Segmentation with Transfer Learning—0
DeepStroke: An Efficient Stroke Screening Framework for Emergency Rooms with Multimodal Adversarial Deep Learning—0
Analysis Towards Classification of Infection and Ischaemia of Diabetic Foot Ulcers—0
Deep Subspace analysing for Semi-Supervised multi-label classification of Diabetic Foot Ulcer—0
Deep Transductive Transfer Learning for Automatic Target Recognition—0
Deep Transfer Clustering of Radio Signals—0
Distilling Generative-Discriminative Representations for Very Low-Resolution Face Recognition—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