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 1951–1975 of 10307 papers

TitleStatusHype
ChemVise: Maximizing Out-of-Distribution Chemical Detection with the Novel Application of Zero-Shot Learning—0
Commonsense Knowledge Transfer for Pre-trained Language Models—0
Accelerating Dependency Graph Learning from Heterogeneous Categorical Event Streams via Knowledge Transfer—0
Char-RNN for Word Stress Detection in East Slavic Languages—0
A Physics-driven GraphSAGE Method for Physical Process Simulations Described by Partial Differential Equations—0
Cross-Domain Label Propagation for Domain Adaptation with Discriminative Graph Self-Learning—0
Comparative Analysis of Deep Learning Models for Crop Disease Detection: A Transfer Learning Approach—0
A Scalable and Generalized Deep Learning Framework for Anomaly Detection in Surveillance Videos—0
Cross-domain Microscopy Cell Counting by Disentangled Transfer Learning—0
Comparative Analysis of Lightweight Deep Learning Models for Memory-Constrained Devices—0
Comparative Analysis of Modality Fusion Approaches for Audio-Visual Person Identification and Verification—0
A Scenario-Based Functional Testing Approach to Improving DNN Performance—0
Cross-Domain Open-Set Machinery Fault Diagnosis Based on Adversarial Network With Multiple Auxiliary Classifiers—0
Comparative Analysis of Transfer Learning in Deep Learning Text-to-Speech Models on a Few-Shot, Low-Resource, Customized Dataset—0
A scoping review of transfer learning research on medical image analysis using ImageNet—0
Cross-modal Knowledge Transfer Learning as Graph Matching Based on Optimal Transport for ASR—0
Characterizing and Understanding the Generalization Error of Transfer Learning with Gibbs Algorithm—0
Characterizing and Avoiding Negative Transfer—0
A Segmentation Foundation Model for Diverse-type Tumors—0
Comparing Male Nyala and Male Kudu Classification using Transfer Learning with ResNet-50 and VGG-16—0
Comparing Transfer and Meta Learning Approaches on a Unified Few-Shot Classification Benchmark—0
Comparing Unsupervised Word Translation Methods Step by Step—0
Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification—0
Comparison of different CNNs for breast tumor classification from ultrasound images—0
The (In)Effectiveness of Intermediate Task Training For Domain Adaptation and Cross-Lingual Transfer 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