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 7701–7750 of 10307 papers

TitleStatusHype
Transfer Learning for Protein Structure Classification at Low ResolutionCode0
Efficient Integration of Multi-channel Information for Speaker-independent Speech Separation—0
Deep Model-Based Reinforcement Learning for High-Dimensional Problems, a Survey—0
One for Many: Transfer Learning for Building HVAC Control—0
Exploring the parameter reusability of CNN—0
Using UNet and PSPNet to explore the reusability principle of CNN parameters—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
Federated Transfer Learning with Dynamic Gradient Aggregation—0
Improving the Accuracy of Global Forecasting Models using Time Series Data Augmentation—0
Fully Automated and Standardized Segmentation of Adipose Tissue Compartments by Deep Learning in Three-dimensional Whole-body MRI of Epidemiological Cohort Studies—0
A Novel Method For Designing Transferable Soft Sensors And Its Application—0
Prompt Agnostic Essay Scorer: A Domain Generalization Approach to Cross-prompt Automated Essay ScoringCode0
An Imitation from Observation Approach to Transfer Learning with Dynamics Mismatch—0
Memory Efficient Class-Incremental Learning for Image Classification—0
Effective Transfer Learning for Identifying Similar Questions: Matching User Questions to COVID-19 FAQs—0
Online Few-shot Gesture Learning on a Neuromorphic Processor—0
A Foliated View of Transfer Learning—0
An Explainable Machine Learning Model for Early Detection of Parkinson's Disease using LIME on DaTscan Imagery—0
FTL: A universal framework for training low-bit DNNs via Feature Transfer—0
AMLN: Adversarial-based Mutual Learning Network for Online Knowledge Distillation—0
Discriminative Partial Domain Adversarial Network—0
Self-supervised Visual Attribute Learning for Fashion Compatibility—0
Noise-Response Analysis of Deep Neural Networks Quantifies Robustness and Fingerprints Structural Malware—0
Multi-label Zero-shot Classification by Learning to Transfer from External Knowledge—0
An Improvement for Capsule Networks using Depthwise Separable Convolution—0
Beyond H-Divergence: Domain Adaptation Theory With Jensen-Shannon Divergence—0
Depressive, Drug Abusive, or Informative: Knowledge-aware Study of News Exposure during COVID-19 Outbreak—0
COVID-19 therapy target discovery with context-aware literature mining—0
Reliable Tuberculosis Detection using Chest X-ray with Deep Learning, Segmentation and Visualization—0
Modular Transfer Learning with Transition Mismatch Compensation for Excessive Disturbance Rejection—0
Force myography benchmark data for hand gesture recognition and transfer learningCode0
Learning Representations for Axis-Aligned Decision Forests through Input Perturbation—0
Practical and sample efficient zero-shot HPO—0
Evaluation of Federated Learning in Phishing Email Detection—0
Reed at SemEval-2020 Task 9: Fine-Tuning and Bag-of-Words Approaches to Code-Mixed Sentiment Analysis—0
Federated Self-Supervised Learning of Multi-Sensor Representations for Embedded Intelligence—0
Real-World Multi-Domain Data Applications for Generalizations to Clinical Settings—0
Dynamic Knowledge Distillation for Black-box Hypothesis Transfer Learning—0
Enhanced Transfer Learning for Autonomous Driving with Systematic Accident Simulation—0
Dog Identification using Soft Biometrics and Neural Networks—0
A Transfer Learning End-to-End ArabicText-To-Speech (TTS) Deep Architecture—0
Effects of Language Relatedness for Cross-lingual Transfer Learning in Character-Based Language Models—0
Camera On-boarding for Person Re-identification using Hypothesis Transfer Learning—0
Leveraging Undiagnosed Data for Glaucoma Classification with Teacher-Student LearningCode0
Multi-task learning for natural language processing in the 2020s: where are we going?—0
XMixup: Efficient Transfer Learning with Auxiliary Samples by Cross-domain Mixup—0
Structure Mapping for Transferability of Causal ModelsCode0
Classification of Diabetic Retinopathy via Fundus Photography: Utilization of Deep Learning Approaches to Speed up Disease Detection—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