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 3801–3825 of 10307 papers

TitleStatusHype
Exploring the Efficacy of Transfer Learning in Mining Image-Based Software Artifacts—0
Exploring the flavor structure of leptons via diffusion models—0
A Multimodal Lightweight Approach to Fault Diagnosis of Induction Motors in High-Dimensional Dataset—0
Few-Shot Learning for Annotation-Efficient Nucleus Instance Segmentation—0
DEEGITS: Deep Learning based Framework for Measuring Heterogenous Traffic State in Challenging Traffic Scenarios—0
Exploring the Limits of Transfer Learning with Unified Model in the Cybersecurity Domain—0
Collaborative Adversarial Learning for RelationalLearning on Multiple Bipartite Graphs—0
Exploring the Low-Resource Transfer-Learning with mT5 model—0
Exploring the Optimization Objective of One-Class Classification for Anomaly Detection—0
Are We Ready for Out-of-Distribution Detection in Digital Pathology?—0
Exploring the Power of Pure Attention Mechanisms in Blind Room Parameter Estimation—0
Collaborative Group Learning—0
Automated Visual Attention Detection using Mobile Eye Tracking in Behavioral Classroom Studies—0
Exploring the structure-property relations of thin-walled, 2D extruded lattices using neural networks—0
Exploring the Transferability of a Foundation Model for Fundus Images: Application to Hypertensive Retinopathy—0
Automated Tomato Maturity Estimation Using an Optimized Residual Model with Pruning and Quantization Techniques—0
Exploring the Uncertainty Properties of Neural Networks’ Implicit Priors in the Infinite-Width Limit—0
Exploring the Uncertainty Properties of Neural Networks' Implicit Priors in the Infinite-Width Limit—0
Exploring the Use of Contrastive Language-Image Pre-Training for Human Posture Classification: Insights from Yoga Pose Analysis—0
Exploring the Viability of Synthetic Query Generation for Relevance Prediction—0
Exploring Transfer Learning and Domain Data Selection for the Biomedical Translation—0
Exploring transfer learning for Deep NLP systems on rarely annotated languages—0
Decouple Non-parametric Knowledge Distillation For End-to-end Speech Translation—0
A Multi-Modal Knowledge-Enhanced Framework for Vessel Trajectory Prediction—0
Few-Shot Learning-Based Human Activity Recognition—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