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 4151–4175 of 10307 papers

TitleStatusHype
Gradient Sparsification For Masked Fine-Tuning of Transformers—0
Eye Disease Classification Using Deep Learning Techniques—0
Novel Batch Active Learning Approach and Its Application to Synthetic Aperture Radar DatasetsCode0
Revisiting invariances and introducing priors in Gromov-Wasserstein distancesCode0
Determination of the critical points for systems of directed percolation class using machine learning—0
FISTNet: FusIon of STyle-path generative Networks for Facial Style Transfer—0
Evaluate Fine-tuning Strategies for Fetal Head Ultrasound Image Segmentation with U-NetCode0
Detecting Throat Cancer from Speech Signals using Machine Learning: A Scoping Literature Review—0
Distilling Knowledge from Text-to-Image Generative Models Improves Visio-Linguistic Reasoning in CLIP—0
Alioth: A Machine Learning Based Interference-Aware Performance Monitor for Multi-Tenancy Applications in Public CloudCode0
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems—0
Study of Vision Transformers for Covid-19 Detection from Chest X-rays—0
Domain Adaptation using Silver Standard Masks for Lateral Ventricle Segmentation in FLAIR MRI—0
Revisiting the Robustness of the Minimum Error Entropy Criterion: A Transfer Learning Case StudyCode0
S2R-ViT for Multi-Agent Cooperative Perception: Bridging the Gap from Simulation to Reality—0
SHAMSUL: Systematic Holistic Analysis to investigate Medical Significance Utilizing Local interpretability methods in deep learning for chest radiography pathology predictionCode0
SoccerKDNet: A Knowledge Distillation Framework for Action Recognition in Soccer Videos—0
MGit: A Model Versioning and Management System—0
Improving BERT with Hybrid Pooling Network and Drop Mask—0
Replay to Remember: Continual Layer-Specific Fine-tuning for German Speech Recognition—0
A Topical Approach to Capturing Customer Insight In Social Media—0
A Scenario-Based Functional Testing Approach to Improving DNN Performance—0
A decision framework for selecting information-transfer strategies in population-based SHM—0
Regression-Oriented Knowledge Distillation for Lightweight Ship Orientation Angle Prediction with Optical Remote Sensing ImagesCode0
Agreement Tracking for Multi-Issue Negotiation Dialogues—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