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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 7701–7725 of 10307 papers

TitleStatusHype
Transfer Learning for Protein Structure Classification at Low ResolutionCode0
Deep Model-Based Reinforcement Learning for High-Dimensional Problems, a Survey—0
Efficient Integration of Multi-channel Information for Speaker-independent Speech Separation—0
One for Many: Transfer Learning for Building HVAC Control—0
Using UNet and PSPNet to explore the reusability principle of CNN parameters—0
Exploring the parameter reusability of CNN—0
Zero-Shot Heterogeneous Transfer Learning from Recommender Systems to Cold-Start Search Retrieval—0
Hybrid Template Canonical Correlation Analysis Method for Enhancing SSVEP Recognition under data-limited Condition—0
A Transfer Learning Method for Speech Emotion Recognition from Automatic Speech Recognition—0
Improving the Accuracy of Global Forecasting Models using Time Series Data Augmentation—0
Federated Transfer Learning with Dynamic Gradient Aggregation—0
A Novel Method For Designing Transferable Soft Sensors And Its Application—0
Fully Automated and Standardized Segmentation of Adipose Tissue Compartments by Deep Learning in Three-dimensional Whole-body MRI of Epidemiological Cohort Studies—0
Prompt Agnostic Essay Scorer: A Domain Generalization Approach to Cross-prompt Automated Essay ScoringCode0
Effective Transfer Learning for Identifying Similar Questions: Matching User Questions to COVID-19 FAQs—0
An Imitation from Observation Approach to Transfer Learning with Dynamics Mismatch—0
Memory Efficient Class-Incremental Learning for Image Classification—0
Online Few-shot Gesture Learning on a Neuromorphic Processor—0
A Foliated View of Transfer Learning—0
Discriminative Partial Domain Adversarial Network—0
FTL: A universal framework for training low-bit DNNs via Feature Transfer—0
An Explainable Machine Learning Model for Early Detection of Parkinson's Disease using LIME on DaTscan Imagery—0
Self-supervised Visual Attribute Learning for Fashion Compatibility—0
AMLN: Adversarial-based Mutual Learning Network for Online Knowledge Distillation—0
Noise-Response Analysis of Deep Neural Networks Quantifies Robustness and Fingerprints Structural Malware—0
Show:102550
← PrevPage 309 of 413Next →
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