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 2126–2150 of 10307 papers

TitleStatusHype
CyFormer: Accurate State-of-Health Prediction of Lithium-Ion Batteries via Cyclic Attention—0
Cardiac Segmentation using Transfer Learning under Respiratory Motion Artifacts—0
A Novel Method for Accurate & Real-time Food Classification: The Synergistic Integration of EfficientNetB7, CBAM, Transfer Learning, and Data Augmentation—0
Acted vs. Improvised: Domain Adaptation for Elicitation Approaches in Audio-Visual Emotion Recognition—0
CarbNN: A Novel Active Transfer Learning Neural Network To Build De Novo Metal Organic Frameworks (MOFs) for Carbon Capture—0
A Novel Implementation of Machine Learning for the Efficient, Explainable Diagnosis of COVID-19 from Chest CT—0
Advancing machine fault diagnosis: A detailed examination of convolutional neural networks—0
Capturing Local and Global Features in Medical Images by Using Ensemble CNN-Transformer—0
Capsule networks for low-data transfer learning—0
A Novel GDP Prediction Technique based on Transfer Learning using CO2 Emission Dataset—0
Cross-Modal Distillation for RGB-Depth Person Re-Identification—0
D3T-GAN: Data-Dependent Domain Transfer GANs for Few-shot Image Generation—0
Developing a Novel Holistic, Personalized Dementia Risk Prediction Model via Integration of Machine Learning and Network Systems Biology Approaches—0
Can You Label Less by Using Out-of-Domain Data? Active & Transfer Learning with Few-shot Instructions—0
A novel machine learning based framework for detection of Autism Spectrum Disorder (ASD)—0
Can We Trust LLMs? Mitigate Overconfidence Bias in LLMs through Knowledge Transfer—0
A novel Facial Recognition technique with Focusing on Masked Faces—0
Advancing Extrapolative Predictions of Material Properties through Learning to Learn—0
A Cantor-Kantorovich Metric Between Markov Decision Processes with Application to Transfer Learning—0
A Novel Ensemble-Based Deep Learning Model with Explainable AI for Accurate Kidney Disease Diagnosis—0
Cantonese Automatic Speech Recognition Using Transfer Learning from Mandarin—0
A Novel DNN Training Framework via Data Sampling and Multi-Task Optimization—0
Can Students Outperform Teachers in Knowledge Distillation based Model Compression?—0
Can Semantic Labels Assist Self-Supervised Visual Representation 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