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 4101–4125 of 10307 papers

TitleStatusHype
Dealing with Adversarial Player Strategies in the Neural Network Game iNNk through Ensemble Learning—0
(DE)^2 CO: Deep Depth Colorization—0
Automated Knot Detection and Pairing for Wood Analysis in the Timber Industry—0
DDTCDR: Deep Dual Transfer Cross Domain Recommendation—0
Automated Identification of Tree Species by Bark Texture Classification Using Convolutional Neural Networks—0
A Multi-input Multi-output Transformer-based Hybrid Neural Network for Multi-class Privacy Disclosure Detection—0
Generative Knowledge Transfer for Neural Language Models—0
DCNNs: A Transfer Learning comparison of Full Weapon Family threat detection for Dual-Energy X-Ray Baggage Imagery—0
DATSING: Data Augmented Time Series Forecasting with Adversarial Domain Adaptation—0
Automated identification of neural cells in the multi-photon images using deep-neural networks—0
A Multi-Format Transfer Learning Model for Event Argument Extraction via Variational Information Bottleneck—0
Automated Extraction of Fine-Grained Standardized Product Information from Unstructured Multilingual Web Data—0
Adaptive transfer learning—0
Generative Adversarial Data Programming—0
Dataset and Performance Comparison of Deep Learning Architectures for Plum Detection and Robotic Harvesting—0
Data-selective Transfer Learning for Multi-Domain Speech Recognition—0
Data Augmentation for Automated Essay Scoring using Transformer Models—0
Data Selection for Efficient Model Update in Federated Learning—0
DaTaSeg: Taming a Universal Multi-Dataset Multi-Task Segmentation Model—0
Automated diagnosis of lung diseases using vision transformer: a comparative study on chest x-ray classification—0
A Multi-Fidelity Graph U-Net Model for Accelerated Physics Simulations—0
Generative Adversarial Imitation Learning for Empathy-based AI—0
Data Scarcity in Recommendation Systems: A Survey—0
Data Quality Monitoring through Transfer Learning on Anomaly Detection for the Hadron Calorimeters—0
A Multi-class Approach -- Building a Visual Classifier based on Textual Descriptions using Zero-Shot 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