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 676–700 of 10307 papers

TitleStatusHype
Rice Leaf Disease Detection: A Comparative Study Between CNN, Transformer and Non-neural Network Architectures—0
Transfer Learning of Tabular Data by Finetuning Large Language Models—0
Mathematics of Digital Twins and Transfer Learning for PDE Models—0
Transforming Social Science Research with Transfer Learning: Social Science Survey Data Integration with AI—0
Capability-Aware Shared Hypernetworks for Flexible Heterogeneous Multi-Robot CoordinationCode0
Super-class guided Transformer for Zero-Shot Attribute ClassificationCode1
Load Forecasting for Households and Energy Communities: Are Deep Learning Models Worth the Effort?Code1
A CT Image Classification Network Framework for Lung Tumors Based on Pre-trained MobileNetV2 Model and Transfer learning, And Its Application and Market Analysis in the Medical field—0
AD-L-JEPA: Self-Supervised Spatial World Models with Joint Embedding Predictive Architecture for Autonomous Driving with LiDAR DataCode1
Patch-GAN Transfer Learning with Reconstructive Models for Cloud Removal—0
Knowledge Transfer in Model-Based Reinforcement Learning Agents for Efficient Multi-Task Learning—0
A Text-Based Knowledge-Embedded Soft Sensing Modeling Approach for General Industrial Process Tasks Based on Large Language Model—0
TADFormer : Task-Adaptive Dynamic Transformer for Efficient Multi-Task Learning—0
Comparison of Neural Models for X-ray Image Classification in COVID-19 Detection—0
Deep Transfer Q-Learning for Offline Non-Stationary Reinforcement Learning—0
Rapid Automated Mapping of Clouds on Titan With Instance SegmentationCode0
Cued Speech Generation Leveraging a Pre-trained Audiovisual Text-to-Speech Model—0
A novel Facial Recognition technique with Focusing on Masked Faces—0
Enhancing Scene Classification in Cloudy Image Scenarios: A Collaborative Transfer Method with Information Regulation Mechanism using Optical Cloud-Covered and SAR Remote Sensing ImagesCode0
DeepVIVONet: Using deep neural operators to optimize sensor locations with application to vortex-induced vibrations—0
Transfer Learning for Deep-Unfolded Combinatorial Optimization Solver with Quantum Annealer—0
SelectiveFinetuning: Enhancing Transfer Learning in Sleep Staging through Selective Domain Alignment—0
A Multimodal Lightweight Approach to Fault Diagnosis of Induction Motors in High-Dimensional Dataset—0
Improving Dialectal Slot and Intent Detection with Auxiliary Tasks: A Multi-Dialectal Bavarian Case StudyCode0
A Diffusion Model and Knowledge Distillation Framework for Robust Coral Detection in Complex Underwater EnvironmentsCode0
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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