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 3876–3900 of 10307 papers

TitleStatusHype
Towards the Fundamental Limits of Knowledge Transfer over Finite Domains—0
GraphControl: Adding Conditional Control to Universal Graph Pre-trained Models for Graph Domain Transfer Learning—0
DeePref: Deep Reinforcement Learning For Video Prefetching In Content Delivery Networks—0
Automatic Control of Reactive Brain Computer Interfaces—0
Domain-invariant Clinical Representation Learning by Bridging Data Distribution Shift across EMR Datasets—0
Advancing Transformer's Capabilities in Commonsense ReasoningCode0
Transfer learning-based physics-informed convolutional neural network for simulating flow in porous media with time-varying controlsCode0
Distributed Transfer Learning with 4th Gen Intel Xeon Processors—0
Model Tuning or Prompt Tuning? A Study of Large Language Models for Clinical Concept and Relation Extraction—0
Geometrically Aligned Transfer Encoder for Inductive Transfer in Regression Tasks—0
Cultural Compass: Predicting Transfer Learning Success in Offensive Language Detection with Cultural FeaturesCode0
HoloFed: Environment-Adaptive Positioning via Multi-band Reconfigurable Holographic Surfaces and Federated Learning—0
Hierarchical Side-Tuning for Vision Transformers—0
Understanding Transfer Learning and Gradient-Based Meta-Learning TechniquesCode0
Empirical Evaluation of the Segment Anything Model (SAM) for Brain Tumor Segmentation—0
Investigating Continuous Learning in Spiking Neural Networks—0
Advancing Diagnostic Precision: Leveraging Machine Learning Techniques for Accurate Detection of Covid-19, Pneumonia, and Tuberculosis in Chest X-Ray Images—0
Comparative Analysis of Transfer Learning in Deep Learning Text-to-Speech Models on a Few-Shot, Low-Resource, Customized Dataset—0
Lifelong Learning for Fog Load Balancing: A Transfer Learning Approach—0
Enhancing Cross-Dataset Performance of Distracted Driving Detection With Score Softmax Classifier And Dynamic Gaussian Smoothing SupervisionCode0
Deep Reinforcement Learning Based Cross-Layer Design in Terahertz Mesh Backhaul Networks—0
Neutral TTS Female Voice Corpus in Brazilian Portuguese—0
X-Transfer: A Transfer Learning-Based Framework for GAN-Generated Fake Image Detection—0
EdgeFD: An Edge-Friendly Drift-Aware Fault Diagnosis System for Industrial IoT—0
CAD Models to Real-World Images: A Practical Approach to Unsupervised Domain Adaptation in Industrial Object ClassificationCode0
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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