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 7351–7375 of 10307 papers

TitleStatusHype
Transfer Learning for Oral Cancer Detection using Microscopic Images—0
Planar 3D Transfer Learning for End to End Unimodal MRI Unbalanced Data SegmentationCode0
Automatic Recognition of the Supraspinatus Tendinopathy from Ultrasound Images using Convolutional Neural Networks—0
Application of Facial Recognition using Convolutional Neural Networks for Entry Access ControlCode0
Ranking Neural Checkpoints—0
Deep Learning for Automatic Quality Grading of Mangoes: Methods and Insights—0
Combining Deep Transfer Learning with Signal-image Encoding for Multi-Modal Mental Wellbeing Classification—0
Cost-effective Variational Active Entity Resolution—0
Efficient Conditional Pre-training for Transfer Learning—0
Interpretable and Transferable Models to Understand the Impact of Lockdown Measures on Local Air QualityCode0
Abnormal Event Detection in Urban Surveillance Videos Using GAN and Transfer Learning—0
Bidirectional RNN-based Few Shot Learning for 3D Medical Image Segmentation—0
Deep learning models for gastric signet ring cell carcinoma classification in whole slide images—0
Effectiveness of Arbitrary Transfer Sets for Data-free Knowledge Distillation—0
A Multi-class Approach -- Building a Visual Classifier based on Textual Descriptions using Zero-Shot Learning—0
Out-of-Task Training for Dialog State Tracking Models—0
Master Thesis: Neural Sign Language Translation by Learning Tokenization—0
Palomino-Ochoa at SemEval-2020 Task 9: Robust System based on Transformer for Code-Mixed Sentiment Classification—0
Continuous Emotion Recognition with Spatiotemporal Convolutional Neural Networks—0
Assistive Diagnostic Tool for Brain Tumor Detection using Computer Vision—0
Probing Predictions on OOD Images via Nearest CategoriesCode0
Deep Learning Based HPV Status Prediction for Oropharyngeal Cancer Patients—0
Can Semantic Labels Assist Self-Supervised Visual Representation Learning?—0
Recognition and standardization of cardiac MRI orientation via multi-tasking learning and deep neural networks—0
Refining Automatic Speech Recognition System for older adults—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