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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 7826–7850 of 10307 papers

TitleStatusHype
DeepTaster: Adversarial Perturbation-Based Fingerprinting to Identify Proprietary Dataset Use in Deep Neural Networks—0
Tracking Universal Features Through Fine-Tuning and Model Merging—0
Traffic control using intelligent timing of traffic lights with reinforcement learning technique and real-time processing of surveillance camera images—0
Traffic Event Detection as a Slot Filling Problem—0
Traffic Flow Estimation using LTE Radio Frequency Counters and Machine Learning—0
Traffic Prediction with Transfer Learning: A Mutual Information-based Approach—0
The Mapillary Traffic Sign Dataset for Detection and Classification on a Global Scale—0
Traffic Signs Detection and Recognition System using Deep Learning—0
Trained Model Fusion for Object Detection using Gating Network—0
Training a Binary Weight Object Detector by Knowledge Transfer for Autonomous Driving—0
Training A Neural Network For Partially Occluded Road Sign Identification In The Context Of Autonomous Vehicles—0
Training Compact Models for Low Resource Entity Tagging using Pre-trained Language Models—0
Training Data Augmentation for Low-Resource Morphological Inflection—0
Targeted transfer learning to improve performance in small medical physics datasets—0
Training general representations for remote sensing using in-domain knowledge—0
Training multi-objective/multi-task collocation physics-informed neural network with student/teachers transfer learnings—0
Towards Reusable Network Components by Learning Compatible Representations—0
Training Novices: The Role of Human-AI Collaboration and Knowledge Transfer—0
Training on the test set? An analysis of Spampinato et al. [arXiv:1609.00344]—0
Training Robots without Robots: Deep Imitation Learning for Master-to-Robot Policy Transfer—0
Training Self-localization Models for Unseen Unfamiliar Places via Teacher-to-Student Data-Free Knowledge Transfer—0
Training Spatial-Frequency Visual Prompts and Probabilistic Clusters for Accurate Black-Box Transfer Learning—0
"Train one, Classify one, Teach one" -- Cross-surgery transfer learning for surgical step recognition—0
Transfer Learning of Semantic Segmentation Methods for Identifying Buried Archaeological Structures on LiDAR Data—0
TransBO: Hyperparameter Optimization via Two-Phase Transfer Learning—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