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 1276–1300 of 10307 papers

TitleStatusHype
On Transfer Learning for a Fully Convolutional Deep Neural SIMO Receiver—0
LLaVA-MoD: Making LLaVA Tiny via MoE Knowledge DistillationCode3
Transfer Learning from Simulated to Real Scenes for Monocular 3D Object Detection—0
Emulating Brain-like Rapid Learning in Neuromorphic Edge ComputingCode0
Auxiliary Input in Training: Incorporating Catheter Features into Deep Learning Models for ECG-Free Dynamic Coronary Roadmapping—0
Q-MRS: A Deep Learning Framework for Quantitative Magnetic Resonance Spectra Analysis—0
Advancing Adversarial Suffix Transfer Learning on Aligned Large Language ModelsCode0
GeoTransfer : Generalizable Few-Shot Multi-View Reconstruction via Transfer Learning—0
Towards Sustainable Personalized On-Device Human Activity Recognition with TinyML and Cloud-Enabled Auto Deployment—0
Comparative Analysis: Violence Recognition from Videos using Transfer LearningCode0
Model Parallel Training and Transfer Learning for Convolutional Neural Networks by Domain Decomposition—0
Application of Neural Ordinary Differential Equations for ITER Burning Plasma DynamicsCode0
Rethinking Knowledge Transfer in Learning Using Privileged InformationCode0
Histology Virtual Staining with Mask-Guided Adversarial Transfer Learning for Tertiary Lymphoid Structure Detection—0
Condensed Sample-Guided Model Inversion for Knowledge Distillation—0
Anatomical Consistency Distillation and Inconsistency Synthesis for Brain Tumor Segmentation with Missing Modalities—0
CNN-Transformer Rectified Collaborative Learning for Medical Image Segmentation—0
Optimal Layer Selection for Latent Data Augmentation—0
Knowledge Graph Modeling-Driven Large Language Model Operating System (LLM OS) for Task Automation in Process Engineering Problem-Solving—0
Deep Learning for Lung Disease Classification Using Transfer Learning and a Customized CNN Architecture with Attention—0
Foundational Model for Electron Micrograph Analysis: Instruction-Tuning Small-Scale Language-and-Vision Assistant for Enterprise Adoption—0
Enhancing Few-Shot Transfer Learning with Optimized Multi-Task Prompt Tuning through Modular Prompt Composition—0
Underwater SONAR Image Classification and Analysis using LIME-based Explainable Artificial IntelligenceCode0
Learning Transferability in Deep Segmentation of Liver Metastases—0
Accounts of using the Tustin-Net architecture on a rotary inverted pendulumCode0
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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