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 5751–5775 of 10307 papers

TitleStatusHype
Strategies in Transfer Learning for Low-Resource Speech Synthesis: Phone Mapping, Features Input, and Source Language Selection—0
Strategy Extraction in Single-Agent Games—0
Stratified Transfer Learning for Cross-domain Activity Recognition—0
Streamlining Brain Tumor Classification with Custom Transfer Learning in MRI Images—0
Structural Similarity for Improved Transfer in Reinforcement Learning—0
Structural Transfer Learning in NL-to-Bash Semantic Parsers—0
Structural transfer learning of non-Gaussian DAG—0
Structure-Aware Transformer Policy for Inhomogeneous Multi-Task Reinforcement Learning—0
Structured Model Probing: Empowering Efficient Transfer Learning by Structured Regularization—0
Structured Variationally Auto-encoded Optimization—0
Student Activity Recognition in Classroom Environments using Transfer Learning—0
Student Network Learning via Evolutionary Knowledge Distillation—0
Student-Oriented Teacher Knowledge Refinement for Knowledge Distillation—0
Student/Teacher Advising through Reward Augmentation—0
Study and development of a Computer-Aided Diagnosis system for classification of chest x-ray images using convolutional neural networks pre-trained for ImageNet and data augmentation—0
Study of Vision Transformers for Covid-19 Detection from Chest X-rays—0
Stuttgart Open Relay Degradation Dataset (SOReDD)—0
StyleInject: Parameter Efficient Tuning of Text-to-Image Diffusion Models—0
Data augmentation in microscopic images for material data mining—0
Style transfer-based image synthesis as an efficient regularization technique in deep learning—0
Stylized Projected GAN: A Novel Architecture for Fast and Realistic Image Generation—0
Subdomain Adaptation with Manifolds Discrepancy Alignment—0
Subgraph Networks Based Contrastive Learning—0
Sub-network Discovery and Soft-masking for Continual Learning of Mixed Tasks—0
Subset Feature Learning for Fine-Grained Category Classification—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