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 9701–9725 of 10307 papers

TitleStatusHype
Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer LearningCode0
Weakly Supervised One-Shot Detection with Attention Similarity Networks—0
Visual and Semantic Knowledge Transfer for Large Scale Semi-supervised Object Detection—0
Recognizing Material Properties from Images—0
HeNet: A Deep Learning Approach on Intel^ Processor Trace for Effective Exploit Detection—0
CityTransfer: Transferring Inter- and Intra-City Knowledge for Chain Store Site Recommendation based on Multi-Source Urban DataCode0
Sample-Efficient Reinforcement Learning through Transfer and Architectural Priors—0
Using reinforcement learning to learn how to play text-based gamesCode0
Optimal Bayesian Transfer Learning—0
NerveNet: Learning Structured Policy with Graph Neural NetworksCode0
Sequence Transfer Learning for Neural Decoding—0
Semantic Segmentation of Human Thigh Quadriceps Muscle in Magnetic Resonance Images—0
Autonomous Vehicle Fleet Coordination With Deep Reinforcement Learning—0
Joint autoencoders: a flexible meta-learning framework—0
Explicit Induction Bias for Transfer Learning with Convolutional Networks—0
Grouping-By-ID: Guarding Against Adversarial Domain Shifts—0
Transfer Learning on Manifolds via Learned Transport Operators—0
Transfer learning for diagnosis of congenital abnormalities of the kidney and urinary tract in children based on Ultrasound imaging data—0
Scalable Multi-Domain Dialogue State TrackingCode0
Learning More Universal Representations for Transfer-LearningCode0
HACS: Human Action Clips and Segments Dataset for Recognition and Temporal LocalizationCode0
Stratified Transfer Learning for Cross-domain Activity Recognition—0
Domain Adaptation Meets Disentangled Representation Learning and Style Transfer—0
Transfer Regression via Pairwise Similarity Regularization—0
Enhance Visual Recognition under Adverse Conditions via Deep Networks—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