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 9076–9100 of 10307 papers

TitleStatusHype
Learning to see across Domains and Modalities—0
Machine Learning on Biomedical Images: Interactive Learning, Transfer Learning, Class Imbalance, and Beyond—0
Improving performance and inference on audio classification tasks using capsule networks—0
Federated Machine Learning: Concept and ApplicationsCode0
Predicting US State-Level Agricultural Sentiment as a Measure of Food Security with Tweets from Farming Communities—0
Predicting Food Security Outcomes Using Convolutional Neural Networks (CNNs) for Satellite Tasking—0
PAC-Bayes Analysis of Sentence Representation—0
Towards Automatic Lesion Classification in the Upper Aerodigestive Tract Using OCT and Deep Transfer Learning Methods—0
OrthographicNet: A Deep Transfer Learning Approach for 3D Object Recognition in Open-Ended DomainsCode0
Size Independent Neural Transfer for RDDL Planning—0
The Actor-Advisor: Policy Gradient With Off-Policy Advice—0
Generative Image Translation for Data Augmentation of Bone Lesion Pathology—0
Transfer Learning From Sound Representations For Anger Detection in Speech—0
Embodied Multimodal Multitask Learning—0
Fine-Grained Temporal Relation Extraction—0
Incremental Learning with Maximum Entropy Regularization: Rethinking Forgetting and Intransigence—0
Funnelling: A New Ensemble Method for Heterogeneous Transfer Learning and its Application to Cross-Lingual Text ClassificationCode0
Real-world Mapping of Gaze Fixations Using Instance Segmentation for Road Construction Safety Applications—0
Detection of Alzheimers Disease from MRI using Convolutional Neural Networks, Exploring Transfer Learning And BellCNN—0
Generalization of feature embeddings transferred from different video anomaly detection domains—0
CoCoNet: A Collaborative Convolutional Network—0
ADMM-SOFTMAX : An ADMM Approach for Multinomial Logistic RegressionCode0
Evaluation of Transfer Learning for Classification of: (1) Diabetic Retinopathy by Digital Fundus Photography and (2) Diabetic Macular Edema, Choroidal Neovascularization and Drusen by Optical Coherence Tomography—0
DELTA: DEep Learning Transfer using Feature Map with Attention for Convolutional NetworksCode0
Surrogate Supervision for Medical Image Analysis: Effective Deep Learning From Limited Quantities of Labeled Data—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