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 4176–4200 of 10307 papers

TitleStatusHype
Prototypical Contrastive Transfer Learning for Multimodal Language Understanding—0
A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directions—0
Uni-Removal: A Semi-Supervised Framework for Simultaneously Addressing Multiple Degradations in Real-World Images—0
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
SimpleMTOD: A Simple Language Model for Multimodal Task-Oriented Dialogue with Symbolic Scene Representation—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
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
Formulation Graphs for Mapping Structure-Composition of Battery Electrolytes to Device Performance—0
PUFFIN: A Path-Unifying Feed-Forward Interfaced Network for Vapor Pressure Prediction—0
To pretrain or not to pretrain? A case study of domain-specific pretraining for semantic segmentation in histopathologyCode0
LogitMat : Zeroshot Learning Algorithm for Recommender Systems without Transfer Learning or Pretrained Models—0
Transfer Learning for the Efficient Detection of COVID-19 from Smartphone Audio DataCode0
Vision Language Transformers: A Survey—0
A Hybrid End-to-End Spatio-Temporal Attention Neural Network with Graph-Smooth Signals for EEG Emotion Recognition—0
Gammatonegram Representation for End-to-End Dysarthric Speech Processing Tasks: Speech Recognition, Speaker Identification, and Intelligibility AssessmentCode0
Self-supervised learning via inter-modal reconstruction and feature projection networks for label-efficient 3D-to-2D segmentationCode0
Exploring Non-Verbal Predicates in Semantic Role Labeling: Challenges and Opportunities—0
Causal Reinforcement Learning: A Survey—0
On Conditional and Compositional Language Model Differentiable Prompting—0
Leveraging Cross-Lingual Transfer Learning in Spoken Named Entity Recognition SystemsCode0
Understanding the Transferability of Representations via Task-RelatednessCode0
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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