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 2876–2900 of 10307 papers

TitleStatusHype
Analysis of three dimensional potential problems in non-homogeneous media with physics-informed deep collocation method using material transfer learning and sensitivity analysis—0
Depth Anywhere: Enhancing 360 Monocular Depth Estimation via Perspective Distillation and Unlabeled Data Augmentation—0
Enhanced Transfer Learning with ImageNet Trained Classification Layer—0
Domain-Invariant Projection Learning for Zero-Shot Recognition—0
Deep learning for affective computing: text-based emotion recognition in decision support—0
An Emotion-Aware Multi-Task Approach to Fake News and Rumour Detection using Transfer Learning—0
DERE: A Task and Domain-Independent Slot Filling Framework for Declarative Relation Extraction—0
Deep Learning-Enabled Sleep Staging From Vital Signs and Activity Measured Using a Near-Infrared Video Camera—0
Designing Category-Level Attributes for Discriminative Visual Recognition—0
Designing ECG Monitoring Healthcare System with Federated Transfer Learning and Explainable AI—0
Autonomous Vehicle Fleet Coordination With Deep Reinforcement Learning—0
Deep learning based Hand gesture recognition system and design of a Human-Machine Interface—0
Design Perspectives of Multitask Deep Learning Models and Applications—0
A Deep Generative Framework for Joint Households and Individuals Population Synthesis—0
Analysis of the Two-Step Heterogeneous Transfer Learning for Laryngeal Blood Vessel Classification: Issue and Improvement—0
A Data-Driven Evolutionary Transfer Optimization for Expensive Problems in Dynamic Environments—0
Detail Preserving Residual Feature Pyramid Modules for Optical Flow—0
Domain Mismatch Doesn't Always Prevent Cross-Lingual Transfer Learning—0
BERT Transformer model for Detecting Arabic GPT2 Auto-Generated Tweets—0
An empirical investigation into audio pipeline approaches for classifying bird species—0
Detecting and Extracting of Adverse Drug Reaction Mentioning Tweets with Multi-Head Self Attention—0
Detecting Bias in Transfer Learning Approaches for Text Classification—0
Detecting Cadastral Boundary from Satellite Images Using U-Net model—0
Domain-specific transfer learning in the automated scoring of tumor-stroma ratio from histopathological images of colorectal cancer—0
Deep-Learning Driven Noise Reduction for Reduced Flux Computed Tomography—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