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 9201–9225 of 10307 papers

TitleStatusHype
Hardware Conditioned Policies for Multi-Robot Transfer LearningCode0
Differential Private Stack Generalization with an Application to Diabetes Prediction—0
CNN based dense underwater 3D scene reconstruction by transfer learning using bubble database—0
SpotTune: Transfer Learning through Adaptive Fine-tuningCode0
DNN Transfer Learning from Diversified Micro-Doppler for Motion Classification—0
Artificial Color Constancy via GoogLeNet with Angular Loss FunctionCode0
A Pretrained DenseNet Encoder for Brain Tumor Segmentation—0
Transfer Learning Using Classification Layer Features of CNN—0
Can Synthetic Faces Undo the Damage of Dataset Bias to Face Recognition and Facial Landmark Detection?Code0
Slum Segmentation and Change Detection : A Deep Learning ApproachCode0
Modularity in biological evolution and evolutionary computation—0
Larger Norm More Transferable: An Adaptive Feature Norm Approach for Unsupervised Domain AdaptationCode0
Transfer Learning with Deep CNNs for Gender Recognition and Age Estimation—0
Evaluation of deep neural networks for traffic sign detection systemsCode0
Transfer Learning for Mixed-Integer Resource Allocation Problems in Wireless Networks—0
Not just a matter of semantics: the relationship between visual similarity and semantic similarity—0
Autonomous Extraction of a Hierarchical Structure of Tasks in Reinforcement Learning, A Sequential Associate Rule Mining Approach—0
Synonym Expansion for Large Shopping Taxonomies—0
Domain Adaptive Transfer Learning with Specialist Models—0
On Deep Domain Adaptation: Some Theoretical Understandings—0
On Generality and Knowledge Transferability in Cross-Domain Duplicate Question Detection for Heterogeneous Community Question Answering—0
On transfer learning using a MAC model variant—0
Probabilistic Random Forest: A machine learning algorithm for noisy datasetsCode0
Performance Estimation of Synthesis Flows cross Technologies using LSTMs and Transfer Learning—0
MT-CGCNN: Integrating Crystal Graph Convolutional Neural Network with Multitask Learning for Material Property PredictionCode0
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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