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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 9101–9125 of 10307 papers

TitleStatusHype
DAC: The Double Actor-Critic Architecture for Learning OptionsCode0
DADA: Deep Adversarial Data Augmentation for Extremely Low Data Regime ClassificationCode0
DAMSL: Domain Agnostic Meta Score-based LearningCode0
Database Workload Characterization with Query Plan EncodersCode0
Data, Depth, and Design: Learning Reliable Models for Skin Lesion AnalysisCode0
Data-driven Prior Learning for Bayesian OptimisationCode0
Data-Efficient Classification of Birdcall Through Convolutional Neural Networks Transfer LearningCode0
Data-Efficient Classification of Radio GalaxiesCode0
Data-Efficient Double-Win Lottery Tickets from Robust Pre-trainingCode0
Data-Efficient Image Recognition with Contrastive Predictive CodingCode0
Data-Free Adversarial DistillationCode0
Dataset Knowledge Transfer for Class-Incremental Learning without MemoryCode0
DATE: Domain Adaptive Product Seeker for E-commerceCode0
Debiasing Graph Transfer Learning via Item Semantic Clustering for Cross-Domain RecommendationsCode0
DeCAF: A Deep Convolutional Activation Feature for Generic Visual RecognitionCode0
Decision support from financial disclosures with deep neural networks and transfer learningCode0
Decoding Neural Responses in Mouse Visual Cortex through a Deep Neural NetworkCode0
Decoupled Self Attention for Accurate One Stage Object DetectionCode0
Decoupling Dynamics and Reward for Transfer LearningCode0
ASMNet: a Lightweight Deep Neural Network for Face Alignment and Pose EstimationCode0
DeepAffinity: Interpretable Deep Learning of Compound-Protein Affinity through Unified Recurrent and Convolutional Neural NetworksCode0
Deep Asymmetric Multi-task Feature LearningCode0
Deep Categorization with Semi-Supervised Self-Organizing MapsCode0
DECAR: Deep Clustering for learning general-purpose Audio RepresentationsCode0
Multi-Target Tracking with Transferable Convolutional Neural NetworksCode0
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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