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 9626–9650 of 10307 papers

TitleStatusHype
Multi-lingual neural title generation for e-Commerce browse pages—0
DOCK: Detecting Objects by transferring Common-sense Knowledge—0
Adversarial Teacher-Student Learning for Unsupervised Domain Adaptation—0
Forecasting Future Humphrey Visual Fields Using Deep LearningCode0
Can Multisensory Cues in VR Help Train Pattern Recognition to Citizen Scientists?—0
HOUDINI: Lifelong Learning as Program SynthesisCode0
Webly Supervised Learning for Skin Lesion Classification—0
Class Subset Selection for Transfer Learning using Submodularity—0
Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task LearningCode0
Reusing Neural Speech Representations for Auditory Emotion Recognition—0
AI Blue Book: Vehicle Price Prediction using Visual FeaturesCode0
PIMKL: Pathway Induced Multiple Kernel Learning—0
Improve the performance of transfer learning without fine-tuning using dissimilarity-based multi-view learning for breast cancer histology images—0
Adversarial Network Compression—0
Learning Deep Representations with Probabilistic Knowledge Transfer—0
Deep Learning Object Detection Methods for Ecological Camera Trap Data—0
Towards Highly Accurate Coral Texture Images Classification Using Deep Convolutional Neural Networks and Data Augmentation—0
Domain transfer convolutional attribute embedding—0
DeepVesselNet: Vessel Segmentation, Centerline Prediction, and Bifurcation Detection in 3-D Angiographic Volumes—0
Importance Weighted Adversarial Nets for Partial Domain Adaptation—0
Feature Transfer Learning for Deep Face Recognition with Under-Represented Data—0
Learning the Localization Function: Machine Learning Approach to Fingerprinting Localization—0
Domain Adaptation with Randomized Expectation MaximizationCode0
Unsupervised Cross-dataset Person Re-identification by Transfer Learning of Spatial-Temporal PatternsCode0
Diagnostic Classification Of Lung Nodules Using 3D Neural NetworksCode0
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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