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 3576–3600 of 10307 papers

TitleStatusHype
Automatic Organization of Neural Modules for Enhanced Collaboration in Neural Networks—0
Enhancing Transfer Learning for Medical Image Classification with SMOTE: A Comparative Study—0
Enhancing Transfer Learning with Flexible Nonparametric Posterior Sampling—0
Classification of Chest Diseases using Wavelet Transforms and Transfer Learning—0
Enhancing Translation for Indigenous Languages: Experiments with Multilingual Models—0
Enhancing Trust in LLMs: Algorithms for Comparing and Interpreting LLMs—0
Classification of Colorectal Cancer Polyps via Transfer Learning and Vision-Based Tactile Sensing—0
Deep cross-domain building extraction for selective depth estimation from oblique aerial imagery—0
Enhancing Wildfire Forecasting Through Multisource Spatio-Temporal Data, Deep Learning, Ensemble Models and Transfer Learning—0
A Multi-Stage Attentive Transfer Learning Framework for Improving COVID-19 Diagnosis—0
Classification of COVID-19 in Chest CT Images using Convolutional Support Vector Machines—0
Adaptive Variance Thresholding: A Novel Approach to Improve Existing Deep Transfer Vision Models and Advance Automatic Knee-Joint Osteoarthritis Classification—0
Approximate Grassmannian Intersections: Subspace-Valued Subspace Learning—0
Ensemble-based Transfer Learning for Low-resource Machine Translation Quality Estimation—0
Self-Distillation Prototypes Network: Learning Robust Speaker Representations without Supervision—0
Ensemble learning and iterative training (ELIT) machine learning: applications towards uncertainty quantification and automated experiment in atom-resolved microscopy—0
Classification of Diabetic Retinopathy Using Unlabeled Data and Knowledge Distillation—0
Classification of Diabetic Retinopathy via Fundus Photography: Utilization of Deep Learning Approaches to Speed up Disease Detection—0
Ensemble of Convolutional Neural Networks for Automatic Grading of Diabetic Retinopathy and Macular Edema—0
A Comparison of Architectures and Pretraining Methods for Contextualized Multilingual Word Embeddings—0
Extremely low-resource machine translation for closely related languages—0
Ensembles of Convolutional Neural Networks models for pediatric pneumonia diagnosis—0
Ensembles of Deep Neural Networks for Action Recognition in Still Images—0
Ensemble Transfer Learning for Emergency Landing Field Identification on Moderate Resource Heterogeneous Kubernetes Cluster—0
FaceLeaks: Inference Attacks against Transfer Learning Models via Black-box Queries—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