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 4926–4950 of 10307 papers

TitleStatusHype
Transferability Estimation Based On Principal Gradient Expectation—0
Transfer Learning with Uncertainty Quantification: Random Effect Calibration of Source to Target (RECaST)—0
Learning deep abdominal CT registration through adaptive loss weighting and synthetic data generationCode0
Inapplicable Actions Learning for Knowledge Transfer in Reinforcement Learning—0
Higher-order Knowledge Transfer for Dynamic Community Detection with Great Changes—0
Learning to Learn: How to Continuously Teach Humans and Machines—0
Automated Detection of Dolphin Whistles with Convolutional Networks and Transfer Learning—0
Hypernetworks for Zero-shot Transfer in Reinforcement Learning—0
Forged Image Detection using SOTA Image Classification Deep Learning Methods for Image Forensics with Error Level Analysis—0
Combined Peak Reduction and Self-Consumption Using Proximal Policy Optimization—0
MNER-QG: An End-to-End MRC framework for Multimodal Named Entity Recognition with Query Grounding—0
Cross-domain Microscopy Cell Counting by Disentangled Transfer Learning—0
An Automatic SOAP Classification System Using Weakly Supervision And Transfer Learning—0
Multi-fidelity Gaussian Process for Biomanufacturing Process Modeling with Small Data—0
Transfer RL via the Undo Maps Formalism—0
Estimation and inference for transfer learning with high-dimensional quantile regression—0
MCFFA-Net: Multi-Contextual Feature Fusion and Attention Guided Network for Apple Foliar Disease Classification—0
Differentially Private Image Classification from FeaturesCode0
Cross-domain Transfer of defect features in technical domains based on partial target data—0
DeepTaster: Adversarial Perturbation-Based Fingerprinting to Identify Proprietary Dataset Use in Deep Neural Networks—0
Semi-Supervised Lifelong Language Learning—0
Agent-Specific Deontic Modality Detection in Legal Language—0
Continual Learning of Natural Language Processing Tasks: A Survey—0
On the impact of incorporating task-information in learning-based image denoising—0
Enhancing team performance with transfer-learning during real-world human-robot collaboration—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