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 6876–6900 of 10307 papers

TitleStatusHype
Automatic Diagnosis of COVID-19 from CT Images using CycleGAN and Transfer Learning—0
Claim Detection in Biomedical Twitter Posts—0
Landmark-Aware and Part-based Ensemble Transfer Learning Network for Facial Expression Recognition from Static images—0
TITAN: T Cell Receptor Specificity Prediction with Bimodal Attention Networks—0
Understanding Synonymous Referring Expressions via Contrastive FeaturesCode0
An Attention-based Weakly Supervised framework for Spitzoid Melanocytic Lesion Diagnosis in WSI—0
A Joint Energy and Latency Framework for Transfer Learning over 5G Industrial Edge Networks—0
Latent-Optimized Adversarial Neural Transfer for Sarcasm DetectionCode0
TeamUNCC@LT-EDI-EACL2021: Hope Speech Detection using Transfer Learning with TransformersCode0
Suppressing simulation bias using multi-modal data—0
Group-Sparse Matrix Factorization for Transfer Learning of Word Embeddings—0
On Training Sketch Recognizers for New Domains—0
Learn Continually, Generalize Rapidly: Lifelong Knowledge Accumulation for Few-shot LearningCode0
Improving Zero-Shot Cross-Lingual Transfer Learning via Robust TrainingCode0
Age Range Estimation using MTCNN and VGG-Face Model—0
Identifying the Limits of Cross-Domain Knowledge Transfer for Pretrained ModelsCode0
Does language help generalization in vision models?Code0
AMMU : A Survey of Transformer-based Biomedical Pretrained Language Models—0
To Share or not to Share: Predicting Sets of Sources for Model Transfer LearningCode0
Personalized Semi-Supervised Federated Learning for Human Activity Recognition—0
Do Deep Neural Networks Forget Facial Action Units? -- Exploring the Effects of Transfer Learning in Health Related Facial Expression Recognition—0
Assessment of deep learning based blood pressure prediction from PPG and rPPG signals—0
Adaptive Sparse Transformer for Multilingual Translation—0
Demystifying BERT: Implications for Accelerator Design—0
Leveraging Label Information in a Knowledge-Driven Approach for Rolling-Element Bearings Remaining Useful Life PredictionCode0
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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