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 3701–3725 of 10307 papers

TitleStatusHype
Cluster, Split, Fuse, and Update: Meta-Learning for Open Compound Domain Adaptive Semantic Segmentation—0
Neuronal and structural differentiation in the emergence of abstract rules in hierarchically modulated spiking neural networks—0
AffecThor at SemEval-2018 Task 1: A cross-linguistic approach to sentiment intensity quantification in tweets—0
Active shooter detection and robust tracking utilizing supplemental synthetic data—0
Clustering Markov Decision Processes For Continual Transfer—0
Clustering-based Multitasking Deep Neural Network for Solar Photovoltaics Power Generation Prediction—0
A Recurrent Neural Network Approach to the Answering Machine Detection Problem—0
Explainable AI in Handwriting Detection for Dyslexia Using Transfer Learning—0
Explainable AI in Diagnosing and Anticipating Leukemia Using Transfer Learning Method—0
A Real Use Case of Semi-Supervised Learning for Mammogram Classification in a Local Clinic of Costa Rica—0
AFFAKT: A Hierarchical Optimal Transport based Method for Affective Facial Knowledge Transfer in Video Deception Detection—0
Explainable AI for Fair Sepsis Mortality Predictive Model—0
Explainable AI based Glaucoma Detection using Transfer Learning and LIME—0
ClusMFL: A Cluster-Enhanced Framework for Modality-Incomplete Multimodal Federated Learning in Brain Imaging Analysis—0
Expert-Free Online Transfer Learning in Multi-Agent Reinforcement Learning—0
Experiments on transfer learning architectures for biomedical relation extraction—0
Experimental and causal view on information integration in autonomous agents—0
Experience Selection Using Dynamics Similarity for Efficient Multi-Source Transfer Learning Between Robots—0
ClueGAIN: Application of Transfer Learning On Generative Adversarial Imputation Nets (GAIN)—0
Explainable AI in Grassland Monitoring: Enhancing Model Performance and Domain Adaptability—0
CLUE: Contextualised Unified Explainable Learning of User Engagement in Video Lectures—0
Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning—0
Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine—0
Explainable Lung Disease Classification from Chest X-Ray Images Utilizing Deep Learning and XAI—0
Active Sentiment Domain Adaptation—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