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 9951–10000 of 10307 papers

TitleStatusHype
FISTNet: FusIon of STyle-path generative Networks for Facial Style Transfer—0
Faces of Experimental Pain: Transferability of Deep Learned Heat Pain Features to Electrical Pain—0
Facial Action Unit Recognition Based on Transfer Learning—0
Facial Anatomical Landmark Detection using Regularized Transfer Learning with Application to Fetal Alcohol Syndrome Recognition—0
Facial Landmark Correlation Analysis—0
Facilitating Reinforcement Learning for Process Control Using Transfer Learning: Overview and Perspectives—0
Factored couplings in multi-marginal optimal transport via difference of convex programming—0
Factors of Influence for Transfer Learning across Diverse Appearance Domains and Task Types—0
FADACS: A Few-shot Adversarial Domain Adaptation Architecture for Context-Aware Parking Availability Sensing—0
FairSTG: Countering performance heterogeneity via collaborative sample-level optimization—0
Fake news detection using parallel BERT deep neural networks—0
Fast Adaptation with Linearized Neural Networks—0
Fast and Accurate Transferability Measurement by Evaluating Intra-class Feature Variance—0
Fast and Efficient DNN Deployment via Deep Gaussian Transfer Learning—0
Fast and Scalable Expansion of Natural Language Understanding Functionality for Intelligent Agents—0
Fast Approach to Build an Automatic Sentiment Annotator for Legal Domain using Transfer Learning—0
Fast Crack Detection Using Convolutional Neural Network—0
Fast CRDNN: Towards on Site Training of Mobile Construction Machines—0
Fast Data-Driven Adaptation of Radar Detection via Meta-Learning—0
Fast End-to-End Speech Recognition via Non-Autoregressive Models and Cross-Modal Knowledge Transferring from BERT—0
Faster Deep Ensemble Averaging for Quantification of DNA Damage from Comet Assay Images With Uncertainty Estimates—0
Faster Uncertainty Quantification for Inverse Problems with Conditional Normalizing Flows—0
Fast Hierarchical Learning for Few-Shot Object Detection—0
Fast Low-parameter Video Activity Localization in Collaborative Learning Environments—0
Fast-staged CNN Model for Accurate pulmonary diseases and Lung cancer detection—0
Fast visual grounding in interaction: bringing few-shot learning with neural networks to an interactive robot—0
Fast Whole-Brain MR Multi-Parametric Mapping with Scan-Specific Self-Supervised Networks—0
FathomVerse: A community science dataset for ocean animal discovery—0
Fault Diagnosis using eXplainable AI: a Transfer Learning-based Approach for Rotating Machinery exploiting Augmented Synthetic Data—0
FBK’s Multilingual Neural Machine Translation System for IWSLT 2017—0
FDA: Feature Decomposition and Aggregation for Robust Airway Segmentation—0
FDSNet: Finger dorsal image spoof detection network using light field camera—0
Feasibility and Transferability of Transfer Learning: A Mathematical Framework—0
Feasibility of Colon Cancer Detection in Confocal Laser Microscopy Images Using Convolution Neural Networks—0
Feasibility of Transfer Learning: A Mathematical Framework—0
Feature Adversarial Distillation for Point Cloud Classification—0
Feature Alignment and Representation Transfer in Knowledge Distillation for Large Language Models—0
Feature Alignment-Based Knowledge Distillation for Efficient Compression of Large Language Models—0
Feature Based Methods in Domain Adaptation for Object Detection: A Review Paper—0
Feature Calibration enhanced Parameter Synthesis for CLIP-based Class-incremental Learning—0
Feature Corrective Transfer Learning: End-to-End Solutions to Object Detection in Non-Ideal Visual Conditions—0
Feature Correlation-guided Knowledge Transfer for Federated Self-supervised Learning—0
Feature Denoising Diffusion Model for Blind Image Quality Assessment—0
Feature discriminativity estimation in CNNs for transfer learning—0
Feature Extraction and Classification from Planetary Science Datasets enabled by Machine Learning—0
Feature-informed Latent Space Regularization for Music Source Separation—0
Feature Interaction Fusion Self-Distillation Network For CTR Prediction—0
Feature matching as improved transfer learning technique for wearable EEG—0
Feature Representation Analysis of Deep Convolutional Neural Network using Two-stage Feature Transfer -An Application for Diffuse Lung Disease Classification-—0
Features are fate: a theory of transfer learning in high-dimensional regression—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