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 7626–7650 of 10307 papers

TitleStatusHype
Measuring Information Transfer in Neural Networks—0
Transfer Learning in Deep Reinforcement Learning: A Survey—0
Multi-structure bone segmentation in pediatric MR images with combined regularization from shape priors and adversarial network—0
3D_DEN: Open-ended 3D Object Recognition using Dynamically Expandable NetworksCode0
Adaptive Label Smoothing—0
Efficient multi-class fetal brain segmentation in high resolution MRI reconstructions with noisy labelsCode0
Transfer learning with class-weighted and focal loss function for automatic skin cancer classification—0
Revisiting the Threat Space for Vision-based Keystroke Inference AttacksCode0
Deep Transfer Learning for Signal Detection in Ambient Backscatter Communications—0
A Comparison of LSTM and BERT for Small Corpus—0
PiaNet: A pyramid input augmented convolutional neural network for GGO detection in 3D lung CT scans—0
SWP-LeafNET: A novel multistage approach for plant leaf identification based on deep CNN—0
Multi-Hop Fact Checking of Political ClaimsCode0
Privacy Analysis of Deep Learning in the Wild: Membership Inference Attacks against Transfer Learning—0
Learning Shape Features and Abstractions in 3D Convolutional Neural Networks for Detecting Alzheimer's Disease—0
Diversified Mutual Learning for Deep Metric Learning—0
Real-time Plant Health Assessment Via Implementing Cloud-based Scalable Transfer Learning On AWS DeepLens—0
kk2018 at SemEval-2020 Task 9: Adversarial Training for Code-Mixing Sentiment Classification—0
Machine Intelligence for Outcome Predictions of Trauma Patients During Emergency Department Care—0
Data-Driven Transferred Energy Management Strategy for Hybrid Electric Vehicles via Deep Reinforcement Learning—0
Driving Tasks Transfer in Deep Reinforcement Learning for Decision-making of Autonomous Vehicles—0
A novel action recognition system for smart monitoring of elderly people using Action Pattern Image and Series CNN with transfer learning—0
Volatility Forecasting with 1-dimensional CNNs via transfer learning—0
BANANA at WNUT-2020 Task 2: Identifying COVID-19 Information on Twitter by Combining Deep Learning and Transfer Learning Models—0
QiaoNing at SemEval-2020 Task 4: Commonsense Validation and Explanation system based on ensemble of language model—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