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 8526–8550 of 10307 papers

TitleStatusHype
Adversarial Domain Adaptation Being Aware of Class Relationships—0
Adversarial Contrastive Distillation with Adaptive Denoising—0
Parameter-efficient is not sufficient: Exploring Parameter, Memory, and Time Efficient Adapter Tuning for Dense Predictions—0
Parameter Efficient Mamba Tuning via Projector-targeted Diagonal-centric Linear Transformation—0
Parameter-Efficient Methods for Metastases Detection from Clinical Notes—0
Parameter-Efficient Sparse Retrievers and Rerankers using Adapters—0
Adversarial attacks on hybrid classical-quantum Deep Learning models for Histopathological Cancer Detection—0
Advantages of biologically-inspired adaptive neural activation in RNNs during learning—0
Subset Feature Learning for Fine-Grained Category Classification—0
Advancing Voice Cloning for Nepali: Leveraging Transfer Learning in a Low-Resource Language—0
Advancing Roadway Sign Detection with YOLO Models and Transfer Learning—0
Parameter-efficient transfer learning of pre-trained Transformer models for speaker verification using adapters—0
Parameter-Efficient Transfer Learning under Federated Learning for Automatic Speech Recognition—0
Advancing Personalized Federated Learning: Integrative Approaches with AI for Enhanced Privacy and Customization—0
Advancing machine fault diagnosis: A detailed examination of convolutional neural networks—0
Advancing Extrapolative Predictions of Material Properties through Learning to Learn—0
Subsidiary Prototype Alignment for Universal Domain Adaptation—0
Parametric Variational Linear Units (PVLUs) in Deep Convolutional Networks—0
Exploring Thermal Images for Object Detection in Underexposure Regions for Autonomous Driving—0
Parasitic Egg Detection and Classification in Low-cost Microscopic Images using Transfer Learning—0
ParCourE: A Parallel Corpus Explorer for a Massively Multilingual Corpus—0
The impact of data set similarity and diversity on transfer learning success in time series forecasting—0
Parkinson's disease diagnostics using AI and natural language knowledge transfer—0
Segmentation of Parotid Gland Tumors Using Multimodal MRI and Contrastive Learning—0
Advancing Efficient Brain Tumor Multi-Class Classification -- New Insights from the Vision Mamba Model in Transfer Learning—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