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 2351–2375 of 10307 papers

TitleStatusHype
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan—0
Boosting Deep Transfer Learning for COVID-19 Classification—0
Boosting Deep Face Recognition via Disentangling Appearance and Geometry—0
An Explainable Deep Learning Framework for Brain Stroke and Tumor Progression via MRI Interpretation—0
Transfer or Self-Supervised? Bridging the Performance Gap in Medical Imaging—0
Boosting Convolutional Neural Networks' Protein Binding Site Prediction Capacity Using SE(3)-invariant transformers, Transfer Learning and Homology-based Augmentation—0
A Diagnostic Model for Acute Lymphoblastic Leukemia Using Metaheuristics and Deep Learning Methods—0
A Computer Vision Approach to Combat Lyme Disease—0
Boosting Automatic COVID-19 Detection Performance with Self-Supervised Learning and Batch Knowledge Ensembling—0
Boosted Zero-Shot Learning with Semantic Correlation Regularization—0
A new semi-supervised inductive transfer learning framework: Co-Transfer—0
A new Potential-Based Reward Shaping for Reinforcement Learning Agent—0
Deep learning for affective computing: text-based emotion recognition in decision support—0
Deep Learning for Automatic Quality Grading of Mangoes: Methods and Insights—0
Deep Learning for identifying radiogenomic associations in breast cancer—0
Deep Learning Models for Classification of COVID-19 Cases by Medical Images—0
DeepMI: Deep Multi-lead ECG Fusion for Identifying Myocardial Infarction and its Occurrence-time—0
Deep Transfer Learning for Few-Shot SAR Image Classification—0
Detecting Throat Cancer from Speech Signals using Machine Learning: A Scoping Literature Review—0
Bone Marrow Cytomorphology Cell Detection using InceptionResNetV2—0
A New Perspective on Smiling and Laughter Detection: Intensity Levels Matter—0
A Deep Value-network Based Approach for Multi-Driver Order Dispatching—0
Bombus Species Image Classification—0
BNS: Building Network Structures Dynamically for Continual Learning—0
A New Multiple Source Domain Adaptation Fault Diagnosis Method between Different Rotating Machines—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