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 1576–1600 of 10307 papers

TitleStatusHype
A Centralized-Distributed Transfer Model for Cross-Domain Recommendation Based on Multi-Source Heterogeneous Transfer Learning—0
Combining Federated Learning and Control: A Survey—0
Combining Image Features and Patient Metadata to Enhance Transfer Learning—0
CommonCanvas: Open Diffusion Models Trained on Creative-Commons Images—0
Monocular Cyclist Detection with Convolutional Neural Networks—0
A3E: Aligned and Augmented Adversarial Ensemble for Accurate, Robust and Privacy-Preserving EEG Decoding—0
Combined Scaling for Zero-shot Transfer Learning—0
A Semi-supervised Approach to Generate the Code-Mixed Text using Pre-trained Encoder and Transfer Learning—0
A Semiparametric Efficient Approach To Label Shift Estimation and Quantification—0
A Game-Theoretic Perspective of Generalization in Reinforcement Learning—0
Combinets: Creativity via Recombination of Neural Networks—0
A Semantics-Guided Class Imbalance Learning Model for Zero-Shot Classification—0
A Self-attention Knowledge Domain Adaptation Network for Commercial Lithium-ion Batteries State-of-health Estimation under Shallow Cycles—0
Against Multifaceted Graph Heterogeneity via Asymmetric Federated Prompt Learning—0
A Segmentation Foundation Model for Diverse-type Tumors—0
A Seed-Augment-Train Framework for Universal Digit Classification—0
Adam Mickiewicz University’s English-Hausa Submissions to the WMT 2021 News Translation Task—0
Domain Shift Analysis in Chest Radiographs Classification in a Veterans Healthcare Administration Population—0
Combining Behaviors with the Successor Features Keyboard—0
A scoping review of transfer learning research on medical image analysis using ImageNet—0
Boosting-GNN: Boosting Algorithm for Graph Networks on Imbalanced Node Classification—0
A Scenario-Based Functional Testing Approach to Improving DNN Performance—0
A Scaling Law for Syn-to-Real Transfer: How Much Is Your Pre-training Effective?—0
Accurate Prostate Cancer Detection and Segmentation on Biparametric MRI using Non-local Mask R-CNN with Histopathological Ground Truth—0
A Scalable and Generalized Deep Learning Framework for Anomaly Detection in Surveillance Videos—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