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 4101–4125 of 10307 papers

TitleStatusHype
Adversarial Vulnerability of Active Transfer Learning—0
Active Learning of Ordinal Embeddings: A User Study on Football Data—0
Epita at SemEval-2018 Task 1: Sentiment Analysis Using Transfer Learning Approach—0
EPITA-ADAPT at SemEval-2019 Task 3: Detecting emotions in textual conversations using deep learning models combination—0
Epistemic Errors of Imperfect Multitask Learners When Distributions Shift—0
Epigenomic language models powered by Cerebras—0
Classification of Microscopy Images of Breast Tissue: Region Duplication based Self-Supervision vs. Off-the Shelf Deep Representations—0
A Preliminary Study on Environmental Sound Classification Leveraging Large-Scale Pretrained Model and Semi-Supervised Learning—0
Environment Sensing-aided Beam Prediction with Transfer Learning for Smart Factory—0
Classification of Melanocytic Nevus Images using BigTransfer (BiT)—0
Bridging Neuroscience and AI: Environmental Enrichment as a Model for Forward Knowledge Transfer—0
EnvBridge: Bridging Diverse Environments with Cross-Environment Knowledge Transfer for Embodied AI—0
Classification of Luminal Subtypes in Full Mammogram Images Using Transfer Learning—0
A predictive physics-aware hybrid reduced order model for reacting flows—0
Adversarial Transfer of Pose Estimation Regression—0
Entropy optimized semi-supervised decomposed vector-quantized variational autoencoder model based on transfer learning for multiclass text classification and generation—0
Classification of Industrial Control Systems screenshots using Transfer Learning—0
Ensemble Transfer Learning of Elastography and B-mode Breast Ultrasound Images—0
Ensemble Transfer Learning for the Prediction of Anti-Cancer Drug Response—0
Classification of Human Monkeypox Disease Using Deep Learning Models and Attention Mechanisms—0
Ensemble Transfer Learning for Multilingual Coreference Resolution—0
Ensemble Transfer Learning for Emergency Landing Field Identification on Moderate Resource Heterogeneous Kubernetes Cluster—0
Classification of Geographical Land Structure Using Convolution Neural Network and Transfer Learning—0
A Practical Guide to Fine-tuning Language Models with Limited Data—0
Adversarial Transfer Learning for Punctuation Restoration—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