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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1851–1875 of 10307 papers

TitleStatusHype
CloudifierNet -- Deep Vision Models for Artificial Image Processing—0
CloudRCA: A Root Cause Analysis Framework for Cloud Computing Platforms—0
A Point in the Right Direction: Vector Prediction for Spatially-aware Self-supervised Volumetric Representation Learning—0
CopyQNN: Quantum Neural Network Extraction Attack under Varying Quantum Noise—0
Core Sampling Framework for Pixel Classification—0
CheX-Nomaly: Segmenting Lung Abnormalities from Chest Radiographs using Machine Learning—0
Adversarial Meta Sampling for Multilingual Low-Resource Speech Recognition—0
ClusMFL: A Cluster-Enhanced Framework for Modality-Incomplete Multimodal Federated Learning in Brain Imaging Analysis—0
A Physics-preserved Transfer Learning Method for Differential Equations—0
A Real Use Case of Semi-Supervised Learning for Mammogram Classification in a Local Clinic of Costa Rica—0
Clustering-based Multitasking Deep Neural Network for Solar Photovoltaics Power Generation Prediction—0
Clustering Markov Decision Processes For Continual Transfer—0
Cluster, Split, Fuse, and Update: Meta-Learning for Open Compound Domain Adaptive Semantic Segmentation—0
CM3T: Framework for Efficient Multimodal Learning for Inhomogeneous Interaction Datasets—0
Chest Disease Detection In X-Ray Images Using Deep Learning Classification Method—0
CMTA: COVID-19 Misinformation Multilingual Analysis on Twitter—0
Action Recognition using Transfer Learning and Majority Voting for CSGO—0
Acceleration of Grokking in Learning Arithmetic Operations via Kolmogorov-Arnold Representation—0
CNN-based approach for glaucoma diagnosis using transfer learning and LBP-based data augmentation—0
CNN based dense underwater 3D scene reconstruction by transfer learning using bubble database—0
COPD Classification in CT Images Using a 3D Convolutional Neural Network—0
ChemVise: Maximizing Out-of-Distribution Chemical Detection with the Novel Application of Zero-Shot Learning—0
Accelerating Dependency Graph Learning from Heterogeneous Categorical Event Streams via Knowledge Transfer—0
CNN-based Survival Model for Pancreatic Ductal Adenocarcinoma in Medical Imaging—0
Char-RNN for Word Stress Detection in East Slavic Languages—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