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 8826–8850 of 10307 papers

TitleStatusHype
Addressing materials' microstructure diversity using transfer learning—0
A 3M-Hybrid Model for the Restoration of Unique Giant Murals: A Case Study on the Murals of Yongle Palace—0
Addressing Dynamic and Sparse Qualitative Data: A Hilbert Space Embedding of Categorical Variables—0
Support-BERT: Predicting Quality of Question-Answer Pairs in MSDN using Deep Bidirectional Transformer—0
Addressing Data Scarcity in Optical Matrix Multiplier Modeling Using Transfer Learning—0
Understanding and Improving Transfer Learning of Deep Models via Neural Collapse—0
Surface EMG-Based Inter-Session/Inter-Subject Gesture Recognition by Leveraging Lightweight All-ConvNet and Transfer Learning—0
Prioritizing Potential Wetland Areas via Region-to-Region Knowledge Transfer and Adaptive Propagation—0
Prior Knowledge for Few-shot Learning—Inductive Reasoning and Distribution Calibration—0
Priors, Hierarchy, and Information Asymmetry for Skill Transfer in Reinforcement Learning—0
Privacy Analysis of Deep Learning in the Wild: Membership Inference Attacks against Transfer Learning—0
Surface-Enhanced Raman Spectroscopy and Transfer Learning Toward Accurate Reconstruction of the Surgical Zone—0
A3CLNN: Spatial, Spectral and Multiscale Attention ConvLSTM Neural Network for Multisource Remote Sensing Data Classification—0
Privacy-Preserving Ensemble Infused Enhanced Deep Neural Network Framework for Edge Cloud Convergence—0
Privacy-Enhanced Training-as-a-Service for On-Device Intelligence: Concept, Architectural Scheme, and Open Problems—0
Privacy-Preserving Transfer Learning for Community Detection using Locally Distributed Multiple Networks—0
Privacy-preserving Transfer Learning via Secure Maximum Mean Discrepancy—0
Private Knowledge Transfer via Model Distillation with Generative Adversarial Networks—0
Private Semi-supervised Knowledge Transfer for Deep Learning from Noisy Labels—0
PrivNet: Safeguarding Private Attributes in Transfer Learning for Recommendation—0
Proactive Guidance of Multi-Turn Conversation in Industrial Search—0
Surf at MEDIQA 2019: Improving Performance of Natural Language Inference in the Clinical Domain by Adopting Pre-trained Language Model—0
Addressing Challenges in Data Quality and Model Generalization for Malaria Detection—0
Probabilistic Meta-Learning for Bayesian Optimization—0
Probabilistic Models of Cross-Lingual Semantic Similarity in Context Based on Latent Cross-Lingual Concepts Induced from Comparable Data—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