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 6701–6725 of 10307 papers

TitleStatusHype
Multirate Training of Neural NetworksCode0
Self-Supervised Learning on 3D Point Clouds by Learning Discrete Generative Models—0
Scalable Differential Privacy With Sparse Network Finetuning—0
Memory Oriented Transfer Learning for Semi-Supervised Image Deraining—0
Practical Transferability Estimation for Image Classification Tasks—0
Learning Graphs for Knowledge Transfer With Limited Labels—0
Cross Modality Knowledge Distillation for Multi-Modal Aerial View Object ClassificationCode0
Informative and Consistent Correspondence Mining for Cross-Domain Weakly Supervised Object Detection—0
Adversarial Training Helps Transfer Learning via Better Representations—0
Recurrent Stacking of Layers in Neural Networks: An Application to Neural Machine Translation—0
Cross-hospital Sepsis Early Detection via Semi-supervised Optimal Transport with Self-paced EnsembleCode0
Toward Fault Detection in Industrial Welding Processes with Deep Learning and Data Augmentation—0
Frustratingly Easy Transferability Estimation—0
Dual-Teacher Class-Incremental Learning With Data-Free Generative Replay—0
Amortized Auto-Tuning: Cost-Efficient Bayesian Transfer Optimization for Hyperparameter RecommendationCode0
A Hands-on Comparison of DNNs for Dialog Separation Using Transfer Learning from Music Source Separation—0
Evolving Image Compositions for Feature Representation Learning—0
A Lightweight ReLU-Based Feature Fusion for Aerial Scene Classification—0
Bilateral Personalized Dialogue Generation with Contrastive LearningCode0
Generating Thermal Human Faces for Physiological Assessment Using Thermal Sensor Auxiliary LabelsCode0
User-specific Adaptive Fine-tuning for Cross-domain Recommendations—0
Why Can You Lay Off Heads? Investigating How BERT Heads Transfer—0
Deep Transfer Learning for Brain Magnetic Resonance Image Multi-class Classification—0
Pre-Trained Models: Past, Present and Future—0
Incorporating Domain Knowledge into Health Recommender Systems using Hyperbolic Embeddings—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