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 3326–3350 of 10307 papers

TitleStatusHype
Deep Integrated Pipeline of Segmentation Guided Classification of Breast Cancer from Ultrasound Images—0
Automatic Mass Detection in Breast Using Deep Convolutional Neural Network and SVM Classifier—0
Epistemic Errors of Imperfect Multitask Learners When Distributions Shift—0
Can Students Outperform Teachers in Knowledge Distillation based Model Compression?—0
Can Semantic Labels Assist Self-Supervised Visual Representation Learning?—0
Effective Domain Knowledge Transfer with Soft Fine-tuning—0
Effective Few-Shot Classification with Transfer Learning—0
Effectiveness of Arbitrary Transfer Sets for Data-free Knowledge Distillation—0
Effectiveness of Mining Audio and Text Pairs from Public Data for Improving ASR Systems for Low-Resource Languages—0
Effective Representations of Clinical Notes—0
Effective training of deep convolutional neural networks for hyperspectral image classification through artificial labeling—0
Effective Transfer Learning for Identifying Similar Questions: Matching User Questions to COVID-19 FAQs—0
Effective Transfer Learning for Low-Resource Natural Language Understanding—0
Effective Two-Stage Knowledge Transfer for Multi-Entity Cross-Domain Recommendation—0
Can You Label Less by Using Out-of-Domain Data? Active & Transfer Learning with Few-shot Instructions—0
Developing a Novel Holistic, Personalized Dementia Risk Prediction Model via Integration of Machine Learning and Network Systems Biology Approaches—0
Deep Implicit Distribution Alignment Networks for Cross-Corpus Speech Emotion Recognition—0
Effects of Additional Data on Bayesian Clustering—0
Effects of Language Relatedness for Cross-lingual Transfer Learning in Character-Based Language Models—0
Effects of Soft-Domain Transfer and Named Entity Information on Deception Detection—0
Capturing Local and Global Features in Medical Images by Using Ensemble CNN-Transformer—0
An adaptive transfer learning perspective on classification in non-stationary environments—0
Efficient Adapter Tuning of Pre-trained Speech Models for Automatic Speaker Verification—0
Efficient and Effective Weight-Ensembling Mixture of Experts for Multi-Task Model Merging—0
Environment Sensing-aided Beam Prediction with Transfer Learning for Smart Factory—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