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 8076–8100 of 10307 papers

TitleStatusHype
Gaze-Net: Appearance-Based Gaze Estimation using Capsule Networks—0
Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-rays—0
Generalizability issues with deep learning models in medicine and their potential solutions: illustrated with Cone-Beam Computed Tomography (CBCT) to Computed Tomography (CT) image conversion—0
Disentangled Adversarial Transfer Learning for Physiological Biosignals—0
Continual Learning for Anomaly Detection in Surveillance Videos—0
Joint Supervised and Self-Supervised Learning for 3D Real-World Challenges—0
Transfer-Learning-Aware Neuro-Evolution for Diseases Detection in Chest X-Ray Images—0
Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection using Chest X-ray—0
Distilling Localization for Self-Supervised Representation Learning—0
A Transfer Learning approach to Heatmap Regression for Action Unit intensity estimation—0
Combined Model for Partially-Observable and Non-Observable Task Switching: Solving Hierarchical Reinforcement Learning Problems Statically and Dynamically with Transfer LearningCode0
Enabling Incremental Knowledge Transfer for Object Detection at the Edge—0
Transfer Learning for EEG-Based Brain-Computer Interfaces: A Review of Progress Made Since 2016—0
Improving Calibration and Out-of-Distribution Detection in Medical Image Segmentation with Convolutional Neural NetworksCode0
Hi Sigma, do I have the Coronavirus?: Call for a New Artificial Intelligence Approach to Support Health Care Professionals Dealing With The COVID-19 Pandemic—0
Fully Automatic Electrocardiogram Classification System based on Generative Adversarial Network with Auxiliary Classifier—0
Exploring Optimal Deep Learning Models for Image-based Malware Variant Classification—0
Deep transfer learning for improving single-EEG arousal detection—0
Beyond Fine-tuning: Few-Sample Sentence Embedding Transfer—0
On the Language Neutrality of Pre-trained Multilingual RepresentationsCode0
Translation Artifacts in Cross-lingual Transfer LearningCode0
Transfer learning and subword sampling for asymmetric-resource one-to-many neural translationCode0
Towards Reusable Network Components by Learning Compatible Representations—0
Adversary Helps: Gradient-based Device-Free Domain-Independent Gesture Recognition—0
COVID_MTNet: COVID-19 Detection with Multi-Task Deep Learning Approaches—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