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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 9801–9825 of 10307 papers

TitleStatusHype
Neural Stain-Style Transfer Learning using GAN for Histopathological ImagesCode0
Listening to the World Improves Speech Command Recognition—0
Distributed Deep Transfer Learning by Basic Probability Assignment—0
Historical Document Image Segmentation with LDA-Initialized Deep Neural NetworksCode0
Multi-Task Domain Adaptation for Deep Learning of Instance Grasping from Simulation—0
Material Classification using Neural Networks—0
Decision support from financial disclosures with deep neural networks and transfer learningCode0
Convolutional Neural Networks for Histopathology Image Classification: Training vs. Using Pre-Trained Networks—0
Neural Program Meta-Induction—0
Using Task Descriptions in Lifelong Machine Learning for Improved Performance and Zero-Shot Transfer—0
A Transfer-Learning Approach for Accelerated MRI using Deep Neural Networks—0
MMCR4NLP: Multilingual Multiway Corpora Repository for Natural Language ProcessingCode0
Deep Convolutional Neural Networks for Interpretable Analysis of EEG Sleep Stage Scoring—0
PUnDA: Probabilistic Unsupervised Domain Adaptation for Knowledge Transfer Across Visual Categories—0
SubUNets: End-To-End Hand Shape and Continuous Sign Language RecognitionCode0
Predictor Combination at Test Time—0
A Study of Convolutional Architectures for Handshape Recognition applied to Sign LanguageCode0
Approximate Grassmannian Intersections: Subspace-Valued Subspace Learning—0
Are we done with object recognition? The iCub robot's perspectiveCode0
An Optimal Online Method of Selecting Source Policies for Reinforcement Learning—0
Constrained Deep Transfer Feature Learning and its Applications—0
Attention-based Wav2Text with Feature Transfer Learning—0
Structured Probabilistic Pruning for Convolutional Neural Network AccelerationCode0
OptionGAN: Learning Joint Reward-Policy Options using Generative Adversarial Inverse Reinforcement LearningCode0
Estimated Depth Map Helps Image ClassificationCode0
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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