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 9001–9025 of 10307 papers

TitleStatusHype
A novel machine learning based framework for detection of Autism Spectrum Disorder (ASD)—0
A Seed-Augment-Train Framework for Universal Digit Classification—0
Colorectal cancer diagnosis from histology images: A comparative study—0
Understanding Unconventional Preprocessors in Deep Convolutional Neural Networks for Face Identification—0
Weighted Multisource Tradaboost—0
On evaluating CNN representations for low resource medical image classification—0
AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree SearchCode0
RecSys-DAN: Discriminative Adversarial Networks for Cross-Domain Recommender Systems—0
Domain Independent SVM for Transfer Learning in Brain Decoding—0
Apple Leaf Disease Identification through Region-of-Interest-Aware Deep Convolutional Neural Network—0
Manifold Criterion Guided Transfer Learning via Intermediate Domain GenerationCode0
Enhanced Transfer Learning with ImageNet Trained Classification Layer—0
Few-Shot Learning-Based Human Activity Recognition—0
Accelerating Deep Unsupervised Domain Adaptation with Transfer Channel Pruning—0
Training Data Independent Image Registration With GANs Using Transfer Learning And Segmentation Information—0
Learning To Avoid Negative Transfer in Few Shot Transfer Learning—0
Automated Classification of Histopathology Images Using Transfer LearningCode0
Automatic Labeling of Data for Transfer Learning—0
What Synthesis is Missing: Depth Adaptation Integrated with Weak Supervision for Indoor Scene Parsing—0
A Machine Learning approach to Risk Minimisation in Electricity Markets with Coregionalized Sparse Gaussian Processes—0
Overcoming Small Minirhizotron Datasets Using Transfer LearningCode0
PPGnet: Deep Network for Device Independent Heart Rate Estimation from Photoplethysmogram—0
Deep Radiomics for Brain Tumor Detection and Classification from Multi-Sequence MRI—0
Cross Domain Knowledge Transfer for Unsupervised Vehicle Re-identification—0
Domain adaptation for holistic skin detection—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