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 4651–4675 of 10307 papers

TitleStatusHype
Humble your Overconfident Networks: Unlearning Overfitting via Sequential Monte Carlo Tempered Deep Ensembles—0
Humpty Dumpty: Controlling Word Meanings via Corpus Poisoning—0
Hunter NMT System for WMT18 Biomedical Translation Task: Transfer Learning in Neural Machine Translation—0
HUST bearing: a practical dataset for ball bearing fault diagnosis—0
HWNet v2: An Efficient Word Image Representation for Handwritten Documents—0
HW-TSC’s Participation at WMT 2020 Quality Estimation Shared Task—0
Hybrid Classical-Quantum Deep Learning Models for Autonomous Vehicle Traffic Image Classification Under Adversarial Attack—0
Hybrid Classical-Quantum method for Diabetic Foot Ulcer Classification—0
HybridCVLNet: A Hybrid CSI Feedback System and its Domain Adaptation—0
Hybrid deep convolution model for lung cancer detection with transfer learning—0
Hybrid deep learning architecture for general disruption prediction across tokamaks—0
Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images—0
Hybrid Inception Architecture with Residual Connection: Fine-tuned Inception-ResNet Deep Learning Model for Lung Inflammation Diagnosis from Chest Radiographs—0
Hybridized Convolutional Neural Networks and Long Short-Term Memory for Improved Alzheimer's Disease Diagnosis from MRI Scans—0
Hybrid-Learning Video Moment Retrieval across Multi-Domain Labels—0
Hybrid quantum transfer learning for crack image classification on NISQ hardware—0
Hybrid Template Canonical Correlation Analysis Method for Enhancing SSVEP Recognition under data-limited Condition—0
HydroDeep -- A Knowledge Guided Deep Neural Network for Geo-Spatiotemporal Data Analysis—0
Hyper Adversarial Tuning for Boosting Adversarial Robustness of Pretrained Large Vision Models—0
Hyperbolic Category Discovery—0
Hyperbolic Knowledge Transfer in Cross-Domain Recommendation System—0
Hypergraph-enhanced Dual Semi-supervised Graph Classification—0
Hypernetworks for Zero-shot Transfer in Reinforcement Learning—0
Hyperparameter Transfer Learning through Surrogate Alignment for Efficient Deep Neural Network Training—0
Hyperparameter Transfer Learning with Adaptive Complexity—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