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 7776–7800 of 10307 papers

TitleStatusHype
CAM-loss: Towards Learning Spatially Discriminative Feature Representations—0
Towards Making Deep Transfer Learning Never Hurt—0
Towards more Reliable Transfer Learning—0
Towards Multimodal Emotion Recognition in German Speech Events in Cars using Transfer Learning—0
Towards Multi-Objective High-Dimensional Feature Selection via Evolutionary Multitasking—0
Towards Network Traffic Monitoring Using Deep Transfer Learning—0
Towards Neural Architecture Search for Transfer Learning in 6G Networks—0
Towards Neural-Network-based optical temperature sensing of Semiconductor Membrane External Cavity Laser—0
Towards Non-task-specific Distillation of BERT via Sentence Representation Approximation—0
Towards On-Device Learning and Reconfigurable Hardware Implementation for Encoded Single-Photon Signal Processing—0
Towards Ophthalmologist Level Accurate Deep Learning System for OCT Screening and Diagnosis—0
Towards Optimal Adapter Placement for Efficient Transfer Learning—0
Towards Practical Single-shot Motion Synthesis—0
Towards Precise Model-free Robotic Grasping with Sim-to-Real Transfer Learning—0
Towards Precision Cardiovascular Analysis in Zebrafish: The ZACAF Paradigm—0
A Novel Training Framework for Physics-informed Neural Networks: Towards Real-time Applications in Ultrafast Ultrasound Blood Flow Imaging—0
Towards Robust and Accurate Visual Prompting—0
Towards Robust Cross-domain Image Understanding with Unsupervised Noise Removal—0
Towards Robust Cross-Domain Recommendation with Joint Identifiability of User Preference—0
Towards Sample-Efficiency and Generalization of Transfer and Inverse Reinforcement Learning: A Comprehensive Literature Review—0
Towards Santali Linguistic Inclusion: Building the First Santali-to-English Translation Model using mT5 Transformer and Data Augmentation—0
Towards scalable efficient on-device ASR with transfer learning—0
Towards Scalable Imitation Learning for Multi-Agent Systems with Graph Neural Networks—0
Towards Scheduling Federated Deep Learning using Meta-Gradients for Inter-Hospital Learning—0
Towards Single-phase Single-stage Detection of Pulmonary Nodules in Chest CT Imaging—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