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 2701–2725 of 10307 papers

TitleStatusHype
Comparative Analysis: Violence Recognition from Videos using Transfer LearningCode0
Application of Neural Ordinary Differential Equations for ITER Burning Plasma DynamicsCode0
Rethinking Knowledge Transfer in Learning Using Privileged InformationCode0
Towards Sustainable Personalized On-Device Human Activity Recognition with TinyML and Cloud-Enabled Auto Deployment—0
Histology Virtual Staining with Mask-Guided Adversarial Transfer Learning for Tertiary Lymphoid Structure Detection—0
Model Parallel Training and Transfer Learning for Convolutional Neural Networks by Domain Decomposition—0
Condensed Sample-Guided Model Inversion for Knowledge Distillation—0
CNN-Transformer Rectified Collaborative Learning for Medical Image Segmentation—0
Anatomical Consistency Distillation and Inconsistency Synthesis for Brain Tumor Segmentation with Missing Modalities—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
Underwater SONAR Image Classification and Analysis using LIME-based Explainable Artificial IntelligenceCode0
Enhancing Few-Shot Transfer Learning with Optimized Multi-Task Prompt Tuning through Modular Prompt Composition—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
Accounts of using the Tustin-Net architecture on a rotary inverted pendulumCode0
Learning Transferability in Deep Segmentation of Liver Metastases—0
Modularized data-driven approximation of the Koopman operator and generator—0
Interactive DualChecker for Mitigating Hallucinations in Distilling Large Language Models—0
Enhanced Infield Agriculture with Interpretable Machine Learning Approaches for Crop Classification—0
RedWhale: An Adapted Korean LLM Through Efficient Continual Pretraining—0
Domain-invariant Progressive Knowledge Distillation for UAV-based Object Detection—0
Embedding Ordinality to Binary Loss Function for Improving Solar Flare ForecastingCode0
Transfer Learning and the Early Estimation of Single-Photon Source Quality using Machine Learning MethodsCode0
Defining Boundaries: The Impact of Domain Specification on Cross-Language and Cross-Domain Transfer in Machine Translation—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