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 4301–4325 of 10307 papers

TitleStatusHype
Commonsense Knowledge Transfer for Pre-trained Language Models—0
An Improved Model for Diabetic Retinopathy Detection by using Transfer Learning and Ensemble Learning—0
Speech Translation with Foundation Models and Optimal Transport: UPC at IWSLT23—0
A new method using deep transfer learning on ECG to predict the response to cardiac resynchronization therapy—0
Distilling Efficient Language-Specific Models for Cross-Lingual TransferCode0
DaTaSeg: Taming a Universal Multi-Dataset Multi-Task Segmentation Model—0
Context selectivity with dynamic availability enables lifelong continual learningCode0
On the Robustness of Arabic Speech Dialect Identification—0
The Effects of Input Type and Pronunciation Dictionary Usage in Transfer Learning for Low-Resource Text-to-Speech—0
Towards Foundation Models for Scientific Machine Learning: Characterizing Scaling and Transfer Behavior—0
TMI! Finetuned Models Leak Private Information from their Pretraining DataCode0
Maximal Domain Independent Representations Improve Transfer Learning—0
Divide, Conquer, and Combine: Mixture of Semantic-Independent Experts for Zero-Shot Dialogue State Tracking—0
Autism Disease Detection Using Transfer Learning Techniques: Performance Comparison Between Central Processing Unit vs Graphics Processing Unit Functions for Neural Networks—0
Improving Polish to English Neural Machine Translation with Transfer Learning: Effects of Data Volume and Language Similarity—0
Transfer Learning for Underrepresented Music Generation—0
Adapting Pre-trained Language Models to Vision-Language Tasks via Dynamic Visual PromptingCode0
Improved Cross-Lingual Transfer Learning For Automatic Speech Translation—0
CL-MRI: Self-Supervised Contrastive Learning to Improve the Accuracy of Undersampled MRI ReconstructionCode0
Hypothesis Transfer Learning with Surrogate Classification Losses: Generalization Bounds through Algorithmic Stability—0
Simple yet Effective Code-Switching Language Identification with Multitask Pre-Training and Transfer Learning—0
VIPriors 3: Visual Inductive Priors for Data-Efficient Deep Learning Challenges—0
Additional Positive Enables Better Representation Learning for Medical Images—0
Deep into The Domain Shift: Transfer Learning through Dependence RegularizationCode0
Pre-Trained Language-Meaning Models for Multilingual Parsing and GenerationCode0
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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