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 4251–4275 of 10307 papers

TitleStatusHype
Meta-Analysis of Transfer Learning for Segmentation of Brain Lesions—0
Transformer Training Strategies for Forecasting Multiple Load Time SeriesCode0
Knowledge Transfer-Driven Few-Shot Class-Incremental LearningCode0
Knowledge Transfer for Dynamic Multi-objective Optimization with a Changing Number of Objectives—0
Learning-based sound speed estimation and aberration correction in linear-array photoacoustic imagingCode0
Persian Semantic Role Labeling Using Transfer Learning and BERT-Based Models—0
Text-Driven Foley Sound Generation With Latent Diffusion ModelCode0
Parameter-efficient is not sufficient: Exploring Parameter, Memory, and Time Efficient Adapter Tuning for Dense Predictions—0
Cross-corpus Readability Compatibility Assessment for English TextsCode0
Modeling T1 Resting-State MRI Variants Using Convolutional Neural Networks in Diagnosis of OCDCode0
DocumentNet: Bridging the Data Gap in Document Pre-Training—0
Understanding and Mitigating Extrapolation Failures in Physics-Informed Neural Networks—0
A Comparison of Self-Supervised Pretraining Approaches for Predicting Disease Risk from Chest Radiograph Images—0
Iterative self-transfer learning: A general methodology for response time-history prediction based on small dataset—0
Solving Large-scale Spatial Problems with Convolutional Neural Networks—0
SMC-UDA: Structure-Modal Constraint for Unsupervised Cross-Domain Renal Segmentation—0
Heterogeneous Continual Learning—0
Monolingual and Cross-Lingual Knowledge Transfer for Topic Classification—0
PersonaPKT: Building Personalized Dialogue Agents via Parameter-efficient Knowledge Transfer—0
Robustness and Generalization Performance of Deep Learning Models on Cyber-Physical Systems: A Comparative StudyCode0
EriBERTa: A Bilingual Pre-Trained Language Model for Clinical Natural Language Processing—0
A Brief Review of Hypernetworks in Deep LearningCode0
Generating Synthetic Datasets by Interpolating along Generalized Geodesics—0
Differentiable Multi-Fidelity Fusion: Efficient Learning of Physics Simulations with Neural Architecture Search and Transfer Learning—0
Parameter-efficient Dysarthric Speech Recognition Using Adapter Fusion and Householder Transformation—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