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 226–250 of 10307 papers

TitleStatusHype
Local Herb Identification Using Transfer Learning: A CNN-Powered Mobile Application for Nepalese Flora—0
Low-Complexity Acoustic Scene Classification with Device Information in the DCASE 2025 ChallengeCode0
A Physics-preserved Transfer Learning Method for Differential Equations—0
GENMO: A GENeralist Model for Human MOtion—0
A Computational Model of Inclusive Pedagogy: From Understanding to Application—0
Transfer Learning-Based Deep Residual Learning for Speech Recognition in Clean and Noisy Environments—0
AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality—0
Explorative Curriculum Learning for Strongly Correlated Electron Systems—0
A Robust Deep Networks based Multi-Object MultiCamera Tracking System for City Scale Traffic—0
Uncertainty-Aware Multi-Expert Knowledge Distillation for Imbalanced Disease Grading—0
Camouflaged Variational Graph AutoEncoder against Attribute Inference Attacks for Cross-Domain Recommendation—0
CAE-DFKD: Bridging the Transferability Gap in Data-Free Knowledge Distillation—0
OpenAVS: Training-Free Open-Vocabulary Audio Visual Segmentation with Foundational Models—0
Multi-level datasets training method in Physics-Informed Neural Networks—0
Redundancy Analysis and Mitigation for Machine Learning-Based Process Monitoring of Additive Manufacturing—0
Head-Tail-Aware KL Divergence in Knowledge Distillation for Spiking Neural Networks—0
Transfer Learning Under High-Dimensional Network Convolutional Regression Model—0
Predicting Stress in Two-phase Random Materials and Super-Resolution Method for Stress Images by Embedding Physical Information—0
Improving Pretrained YAMNet for Enhanced Speech Command Detection via Transfer Learning—0
Unifying Direct and Indirect Learning for Safe Control of Linear Systems—0
Post-Transfer Learning Statistical Inference in High-Dimensional Regression—0
NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation—0
An Explainable Nature-Inspired Framework for Monkeypox Diagnosis: Xception Features Combined with NGBoost and African Vultures Optimization Algorithm—0
On the workflow, opportunities and challenges of developing foundation model in geophysics—0
Improving Local Air Quality Predictions Using Transfer Learning on Satellite Data and Graph Neural Networks—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