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 3051–3075 of 10307 papers

TitleStatusHype
Enhancing Biomedical Text Summarization and Question-Answering: On the Utility of Domain-Specific Pre-Training—0
Advances and Challenges in Meta-Learning: A Technical Review—0
Minimax Excess Risk of First-Order Methods for Statistical Learning with Data-Dependent Oracles—0
Integrating Curricula with Replays: Its Effects on Continual LearningCode0
Building and Road Segmentation Using EffUNet and Transfer Learning Approach—0
Formulation Graphs for Mapping Structure-Composition of Battery Electrolytes to Device Performance—0
Transfer Learning of Semantic Segmentation Methods for Identifying Buried Archaeological Structures on LiDAR Data—0
Distilling Universal and Joint Knowledge for Cross-Domain Model Compression on Time Series DataCode0
LogitMat : Zeroshot Learning Algorithm for Recommender Systems without Transfer Learning or Pretrained Models—0
Gammatonegram Representation for End-to-End Dysarthric Speech Processing Tasks: Speech Recognition, Speaker Identification, and Intelligibility AssessmentCode0
Vision Language Transformers: A Survey—0
To pretrain or not to pretrain? A case study of domain-specific pretraining for semantic segmentation in histopathologyCode0
PUFFIN: A Path-Unifying Feed-Forward Interfaced Network for Vapor Pressure Prediction—0
Transfer Learning for the Efficient Detection of COVID-19 from Smartphone Audio DataCode0
Self-supervised learning via inter-modal reconstruction and feature projection networks for label-efficient 3D-to-2D segmentationCode0
A Hybrid End-to-End Spatio-Temporal Attention Neural Network with Graph-Smooth Signals for EEG Emotion Recognition—0
MDViT: Multi-domain Vision Transformer for Small Medical Image Segmentation DatasetsCode1
On Conditional and Compositional Language Model Differentiable Prompting—0
Causal Reinforcement Learning: A Survey—0
Exploring Non-Verbal Predicates in Semantic Role Labeling: Challenges and Opportunities—0
Leveraging Cross-Lingual Transfer Learning in Spoken Named Entity Recognition SystemsCode0
Understanding the Transferability of Representations via Task-RelatednessCode0
SAM-DA: UAV Tracks Anything at Night with SAM-Powered Domain AdaptationCode1
Autism Spectrum Disorder Classification in Children based on Structural MRI Features Extracted using Contrastive Variational Autoencoder—0
Transfer learning for semantic similarity measures based on symbolic regressionCode0
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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