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 1701–1725 of 10307 papers

TitleStatusHype
A Privacy-Preserving Framework with Multi-Modal Data for Cross-Domain Recommendation—0
Adverse Drug Reaction Detection in Twitter Using RoBERTa and Rules—0
A Privacy-Preserving Domain Adversarial Federated learning for multi-site brain functional connectivity analysis—0
A privacy-preserving data storage and service framework based on deep learning and blockchain for construction workers' wearable IoT sensors—0
Adversary ML Resilience in Autonomous Driving Through Human Centered Perception Mechanisms—0
Active Learning with Transfer Learning—0
A Prior Knowledge Based Tumor and Tumoral Subregion Segmentation Tool for Pediatric Brain Tumors—0
A Primer on Pretrained Multilingual Language Models—0
Adversary Helps: Gradient-based Device-Free Domain-Independent Gesture Recognition—0
A Pretrained DenseNet Encoder for Brain Tumor Segmentation—0
Adversarial Vulnerability of Active Transfer Learning—0
Active Learning of Ordinal Embeddings: A User Study on Football Data—0
Accelerating Matrix Diagonalization through Decision Transformers with Epsilon-Greedy Optimization—0
A Preliminary Study on Environmental Sound Classification Leveraging Large-Scale Pretrained Model and Semi-Supervised Learning—0
A predictive physics-aware hybrid reduced order model for reacting flows—0
Adversarial Transfer of Pose Estimation Regression—0
Adversarial Transfer Learning for Punctuation Restoration—0
A Practical Guide to Fine-tuning Language Models with Limited Data—0
Active Learning for Sequence Tagging with Deep Pre-trained Models and Bayesian Uncertainty Estimates—0
Conformal Prediction Under Generalized Covariate Shift with Posterior Drift—0
A Practical Approach towards Causality Mining in Clinical Text using Active Transfer Learning—0
Approximation by non-symmetric networks for cross-domain learning—0
Active Learning for Rumor Identification on Social Media—0
Approximate Grassmannian Intersections: Subspace-Valued Subspace Learning—0
Approximated Prompt Tuning for Vision-Language Pre-trained Models—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