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 5826–5850 of 10307 papers

TitleStatusHype
Plan Your Target and Learn Your Skills: Transferable State-Only Imitation Learning via Decoupled Policy OptimizationCode0
On partitioning of an SHM problem and parallels with transfer learning—0
Zero-shot Transfer Learning within a Heterogeneous Graph via Knowledge Transfer NetworksCode0
Transfer Learning of High-Fidelity Opacity Spectra in Autoencoders and Surrogate Models—0
Visual Feature Encoding for GNNs on Road Networks—0
Self-supervised Transformer for Deepfake Detection—0
Hyperspectral Pixel Unmixing with Latent Dirichlet Variational Autoencoder—0
A Versatile Agent for Fast Learning from Human Instructors—0
Transfer Learning Algorithm with Knowledge Division LevelCode0
Improving Response Time of Home IoT Services in Federated LearningCode0
A Multimodal German Dataset for Automatic Lip Reading Systems and Transfer Learning—0
BioADAPT-MRC: Adversarial Learning-based Domain Adaptation Improves Biomedical Machine Reading Comprehension TaskCode0
A Deep Learning Approach for Network-wide Dynamic Traffic Prediction during Hurricane Evacuation—0
Learn From the Past: Experience Ensemble Knowledge Distillation—0
A Survey of Multilingual Models for Automatic Speech Recognition—0
The Reality of Multi-Lingual Machine Translation—0
Oolong: Investigating What Makes Transfer Learning Hard with Controlled StudiesCode0
Collaborative Training of Heterogeneous Reinforcement Learning Agents in Environments with Sparse Rewards: What and When to Share?Code0
Towards Unsupervised Domain Adaptation via Domain-Transformer—0
Absolute Zero-Shot LearningCode0
A Class of Geometric Structures in Transfer Learning: Minimax Bounds and Optimality—0
Multi-fidelity reinforcement learning framework for shape optimization—0
Domain-Augmented Domain Adaptation—0
Simplified Learning of CAD Features Leveraging a Deep Residual AutoencoderCode0
Probabilities of the Third Type: Statistical Relational Learning and Reasoning with Relative Frequencies—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