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 3901–3950 of 10307 papers

TitleStatusHype
Evaluation of Transfer Learning for Adverse Drug Event (ADE) and Medication Entity Extraction—0
Evaluation of Transfer Learning for Classification of: (1) Diabetic Retinopathy by Digital Fundus Photography and (2) Diabetic Macular Edema, Choroidal Neovascularization and Drusen by Optical Coherence Tomography—0
CLIP-aware Domain-Adaptive Super-Resolution—0
Fast Low-parameter Video Activity Localization in Collaborative Learning Environments—0
Evaluation of Transfer Learning and Domain Adaptation for Analyzing German-Speaking Job Advertisements—0
Evaluation of taxonomic and neural embedding methods for calculating semantic similarity—0
CLIP also Understands Text: Prompting CLIP for Phrase Understanding—0
Evaluation of Deep Learning based Pose Estimation for Sign Language Recognition—0
Evaluation and Comparison of Emotionally Evocative Image Augmentation Methods—0
Fast Whole-Brain MR Multi-Parametric Mapping with Scan-Specific Self-Supervised Networks—0
Clinical Risk Prediction Using Language Models: Benefits And Considerations—0
Fault Diagnosis using eXplainable AI: a Transfer Learning-based Approach for Rotating Machinery exploiting Augmented Synthetic Data—0
Aerodynamic and structural airfoil shape optimisation via Transfer Learning-enhanced Deep Reinforcement Learning—0
Active Multitask Learning with Committees—0
Evaluating zero-shot transfers and multilingual models for dependency parsing and POS tagging within the low-resource language family Tupían—0
FDA: Feature Decomposition and Aggregation for Robust Airway Segmentation—0
Evaluating Unsupervised Representation Learning for Detecting Stances of Fake News—0
Feasibility and Transferability of Transfer Learning: A Mathematical Framework—0
Clinical Document Classification Using Labeled and Unlabeled Data Across Hospitals—0
Feasibility of Transfer Learning: A Mathematical Framework—0
Transfer Learning for Wildlife Classification: Evaluating YOLOv8 against DenseNet, ResNet, and VGGNet on a Custom Dataset—0
Feature Adversarial Distillation for Point Cloud Classification—0
Feature Alignment and Representation Transfer in Knowledge Distillation for Large Language Models—0
Feature Alignment-Based Knowledge Distillation for Efficient Compression of Large Language Models—0
Evaluating Transferability in Retrieval Tasks: An Approach Using MMD and Kernel Methods—0
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