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 6876–6900 of 10307 papers

TitleStatusHype
Sharper Reasons: Argument Mining Leveraged with Confluent Knowledge—0
Large Scale Transfer Learning for Differentially Private Image Classification—0
Large-scale Transfer Learning for Low-resource Spoken Language Understanding—0
Transfer Learning with Edge Attention for Prostate MRI Segmentation—0
An Optimal Online Method of Selecting Source Policies for Reinforcement Learning—0
The Power of Training: How Different Neural Network Setups Influence the Energy Demand—0
SHM-Traffic: DRL and Transfer learning based UAV Control for Structural Health Monitoring of Bridges with Traffic—0
LA-SACo: A Study of Learning Approaches for Sentiments Analysis inCode-Mixing Texts—0
LastResort at SemEval-2022 Task 5: Towards Misogyny Identification using Visual Linguistic Model Ensembles And Task-Specific Pretraining—0
Latent Alignment with Deep Set EEG Decoders—0
Anomaly Detection Using Computer Vision: A Comparative Analysis of Class Distinction and Performance Metrics—0
Latent Function Decomposition for Forecasting Li-ion Battery Cells Capacity: A Multi-Output Convolved Gaussian Process Approach—0
Latent Hinge-Minimax Risk Minimization for Inference from a Small Number of Training Samples—0
Latent-Insensitive autoencoders for Anomaly Detection—0
Latent Intuitive Physics: Learning to Transfer Hidden Physics from A 3D Video—0
Latent Object Characteristics Recognition with Visual to Haptic-Audio Cross-modal Transfer Learning—0
Short Text Clustering with Transformers—0
Latent User Linking for Collaborative Cross Domain Recommendation—0
Anomaly Detection through Transfer Learning in Agriculture and Manufacturing IoT Systems—0
Lautum Regularization for Semi-supervised Transfer Learning—0
A Complete Recipe for Bayesian Knowledge Transfer: Object Tracking—0
LaViP:Language-Grounded Visual Prompts—0
LAWDR: Language-Agnostic Weighted Document Representations from Pre-trained Models—0
Layer by Layer: Uncovering Where Multi-Task Learning Happens in Instruction-Tuned Large Language Models—0
Anomaly Detection in Automatic Generation Control Systems Based on Traffic Pattern Analysis and Deep 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