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 2476–2500 of 10307 papers

TitleStatusHype
A Comprehensive Survey of Multilingual Neural Machine Translation—0
Beyond Glucose-Only Assessment: Advancing Nocturnal Hypoglycemia Prediction in Children with Type 1 Diabetes—0
Beyond Flatland: Pre-training with a Strong 3D Inductive Bias—0
An Empirical Study on Measuring the Similarity of Sentential Arguments with Language Model Domain Adaptation—0
Deep Transfer Learning for Few-Shot SAR Image Classification—0
Deep transfer learning for image classification: a survey—0
Beyond Fine-tuning: Classifying High Resolution Mammograms using Function-Preserving Transformations—0
Beyond Fine Tuning: A Modular Approach to Learning on Small Data—0
Beyond Efficiency: Molecular Data Pruning for Enhanced Generalization—0
A Deep Learning Approach for Real-Time 3D Human Action Recognition from Skeletal Data—0
A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities—0
BEV-Seg: Bird's Eye View Semantic Segmentation Using Geometry and Semantic Point Cloud—0
Between-Domain Instance Transition Via the Process of Gibbs Sampling in RBM—0
An Empirical Study on Cross-Lingual and Cross-Domain Transfer for Legal Judgment Prediction—0
Better Transfer Learning with Inferred Successor Maps—0
Better and Faster: Knowledge Transfer from Multiple Self-supervised Learning Tasks via Graph Distillation for Video Classification—0
An Empirical Study of Scaling Laws for Transfer—0
A Deep Learning Approach for Network-wide Dynamic Traffic Prediction during Hurricane Evacuation—0
Deep Face Recognition Model Compression via Knowledge Transfer and Distillation—0
Deep transfer learning for improving single-EEG arousal detection—0
Best Practices in Convolutional Networks for Forward-Looking Sonar Image Recognition—0
Best Practices for Learning Domain-Specific Cross-Lingual Embeddings—0
An Empirical Study of Language Relatedness for Transfer Learning in Neural Machine Translation—0
A Deep Learning Approach for Diabetic Retinopathy detection using Transfer Learning—0
Recent Few-Shot Object Detection Algorithms: A Survey with Performance Comparison—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