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 3926–3950 of 10307 papers

TitleStatusHype
Clinical Concept and Relation Extraction Using Prompt-based Machine Reading Comprehension—0
A Proper Orthogonal Decomposition approach for parameters reduction of Single Shot Detector networks—0
Evaluating Transferability for Covid 3D Localization Using CT SARS-CoV-2 segmentation models—0
Evaluating the Transferability and Adversarial Discrimination of Convolutional Neural Networks for Threat Object Detection and Classification within X-Ray Security Imagery—0
Feature Correlation-guided Knowledge Transfer for Federated Self-supervised Learning—0
Evaluating the structure of cognitive tasks with transfer learning—0
Cliff-Learning—0
A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT—0
Evaluating the Performance of StyleGAN2-ADA on Medical Images—0
Evaluating the Impact of Model Scale for Compositional Generalization in Semantic Parsing—0
Feature-informed Latent Space Regularization for Music Source Separation—0
Client Clustering Meets Knowledge Sharing: Enhancing Privacy and Robustness in Personalized Peer-to-Peer Learning—0
Evaluating the Cross-Lingual Effectiveness of Massively Multilingual Neural Machine Translation—0
Evaluating Standard and Dialectal Frisian ASR: Multilingual Fine-tuning and Language Identification for Improved Low-resource Performance—0
CLICKER: Attention-Based Cross-Lingual Commonsense Knowledge Transfer—0
Feature Representation Analysis of Deep Convolutional Neural Network using Two-stage Feature Transfer -An Application for Diffuse Lung Disease Classification-—0
Features are fate: a theory of transfer learning in high-dimensional regression—0
Feature Space Transfer for Data Augmentation—0
Feature-Supervised Action Modality Transfer—0
Feature Transfer Learning for Deep Face Recognition with Under-Represented Data—0
Feature Transfer Learning for Face Recognition With Under-Represented Data—0
Feature Transformation Ensemble Model with Batch Spectral Regularization for Cross-Domain Few-Shot Classification—0
FedAL: Black-Box Federated Knowledge Distillation Enabled by Adversarial Learning—0
A Progressive Transformer for Unifying Binary Code Embedding and Knowledge Transfer—0
Evaluating Query Efficiency and Accuracy of Transfer Learning-based Model Extraction Attack in Federated Learning—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