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 8276–8300 of 10307 papers

TitleStatusHype
Estimating Q(s,s') with Deterministic Dynamics Gradients—0
Learning Attentive Meta-Transfer—0
Generative Adversarial Networks For Data Scarcity Industrial Positron Images With Attention—0
Homogeneous Online Transfer Learning with Online Distribution Discrepancy MinimizationCode0
Prediction of wall-bounded turbulence from wall quantities using convolutional neural networks—0
Quantifying the Performance of Federated Transfer Learning—0
Machine Learning based Post Processing Artifact Reduction in HEVC Intra Coding—0
Improved Multi-Stage Training of Online Attention-based Encoder-Decoder Models—0
Deep neural network models for computational histopathology: A survey—0
Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative StudyCode0
Transfer Learning for Brain Tumor Segmentation—0
Transfer Learning in General Lensless Imaging through Scattering Media—0
Towards Deep Federated Defenses Against Malware in Cloud Ecosystems—0
Deep Transfer Learning Based Downlink Channel Prediction for FDD Massive MIMO SystemsCode0
Neural Subgraph Isomorphism CountingCode0
Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer—0
Intelligent Condition Based Monitoring Techniques for Bearing Fault Diagnosis—0
Federated Imitation Learning: A Novel Framework for Cloud Robotic Systems with Heterogeneous Sensor Data—0
Data-Free Adversarial DistillationCode0
Destruction of Image Steganography using Generative Adversarial NetworksCode0
Transfer Learning with Edge Attention for Prostate MRI Segmentation—0
Progressive transfer learning for low frequency data prediction in full waveform inversion—0
Image Analytics for Legal Document Review: A Transfer Learning Approach—0
TransMatch: A Transfer-Learning Scheme for Semi-Supervised Few-Shot Learning—0
Pruning by Explaining: A Novel Criterion for Deep Neural Network PruningCode0
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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