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 6251–6275 of 10307 papers

TitleStatusHype
Successor Feature Representations—0
AI-Powered Semantic Segmentation and Fluid Volume Calculation of Lung CT images in Covid-19 Patients—0
A deep convolutional neural network for classification of Aedes albopictus mosquitoes—0
Domain Agnostic Few-Shot Learning For Document Intelligence—0
Deep Learning Analysis of Cardiac MRI in Legacy Datasets: Multi-Ethnic Study of Atherosclerosis—0
Generating Table Vector Representations—0
OpeNPDN: A Neural-network-based Framework for Power Delivery Network Synthesis—0
Pay attention to emoji: Feature Fusion Network with EmoGraph2vec Model for Sentiment Analysis—0
Revisit Multimodal Meta-Learning through the Lens of Multi-Task LearningCode0
Deep Transfer Learning for Multi-source Entity Linkage via Domain AdaptationCode0
NIDA-CLIFGAN: Natural Infrastructure Damage Assessment through Efficient Classification Combining Contrastive Learning, Information Fusion and Generative Adversarial Networks—0
Transfer learning with causal counterfactual reasoning in Decision Transformers—0
Zero-Shot Action Recognition from Diverse Object-Scene CompositionsCode0
Transferring Domain-Agnostic Knowledge in Video Question Answering—0
A Metalearning Approach for Physics-Informed Neural Networks (PINNs): Application to Parameterized PDEs—0
Improving the efficacy of Deep Learning models for Heart Beat detection on heterogeneous datasetsCode0
Deep Integrated Pipeline of Segmentation Guided Classification of Breast Cancer from Ultrasound Images—0
BioIE: Biomedical Information Extraction with Multi-head Attention Enhanced Graph Convolutional Network—0
Self-Supervised Knowledge Transfer via Loosely Supervised Auxiliary Tasks—0
Review-Based Domain Disentanglement without Duplicate Users or Contexts for Cross-Domain Recommendation—0
Covariance-Generalized Matching Component Analysis for Data Fusion and Transfer Learning—0
Latent-Insensitive autoencoders for Anomaly Detection—0
Age and Gender Prediction using Deep CNNs and Transfer Learning—0
A Deep Reinforcement Learning Approach for Audio-based Navigation and Audio Source Localization in Multi-speaker Environments—0
Novel coronavirus pneumonia lesion segmentation in CT images—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