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 9451–9475 of 10307 papers

TitleStatusHype
BhamNLP at SemEval-2020 Task 12: An Ensemble of Different Word Embeddings and Emotion Transfer Learning for Arabic Offensive Language Identification in Social Media—0
Contrastive Learning and Cycle Consistency-based Transductive Transfer Learning for Target Annotation—0
Contrastive Learning and Self-Training for Unsupervised Domain Adaptation in Semantic Segmentation—0
Contrastive learning for unsupervised medical image clustering and reconstruction—0
Contrastive Learning Meets Transfer Learning: A Case Study In Medical Image Analysis—0
Synthetic Image Data for Deep Learning—0
Real-World Image Super Resolution via Unsupervised Bi-directional Cycle Domain Transfer Learning based Generative Adversarial Network—0
Be Your Own Best Competitor! Multi-Branched Adversarial Knowledge Transfer—0
Real-world Mapping of Gaze Fixations Using Instance Segmentation for Road Construction Safety Applications—0
A Comprehensive Survey of Sentence Representations: From the BERT Epoch to the ChatGPT Era and Beyond—0
Contrastive Representation Distillation via Multi-Scale Feature Decoupling—0
Contrastive Representation Learning Helps Cross-institutional Knowledge Transfer: A Study in Pediatric Ventilation Management—0
Real-World Multi-Domain Data Applications for Generalizations to Clinical Settings—0
Breaking Writer's Block: Low-cost Fine-tuning of Natural Language Generation Models—0
Controlling Neural Collapse Enhances Out-of-Distribution Detection and Transfer Learning—0
Controlling the Precision-Recall Tradeoff in Differential Dependency Network Analysis—0
Control Theoretic Approach to Fine-Tuning and Transfer Learning—0
Control-Theoretic Techniques for Online Adaptation of Deep Neural Networks in Dynamical Systems—0
Beyond Vanilla Fine-Tuning: Leveraging Multistage, Multilingual, and Domain-Specific Methods for Low-Resource Machine Translation—0
ConVAEr: Convolutional Variational AutoEncodeRs for incremental similarity learning—0
Convergence and Implicit Bias of Gradient Descent on Continual Linear Classification—0
Adaptive Physics-informed Neural Networks: A Survey—0
Conversational Bots for Psychotherapy: A Study of Generative Transformer Models Using Domain-specific Dialogues—0
Conversion and Implementation of State-of-the-Art Deep Learning Algorithms for the Classification of Diabetic Retinopathy—0
Beyond Transfer Learning: Co-finetuning for Action Localisation—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