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 3551–3575 of 10307 papers

TitleStatusHype
Deep Doubly Supervised Transfer Network for Diagnosis of Breast Cancer with Imbalanced Ultrasound Imaging Modalities—0
Deep Discriminative Fine-Tuning for Cancer Type Classification—0
Automatic detection of passable roads after floods in remote sensed and social media data—0
Classical-to-Quantum Transfer Learning for Spoken Command Recognition Based on Quantum Neural Networks—0
Enhancing Industrial Transfer Learning with Style Filter: Cost Reduction and Defect-Focus—0
Enhancing Instance-Level Image Classification with Set-Level Labels—0
Classification Algorithm of Speech Data of Parkinsons Disease Based on Convolution Sparse Kernel Transfer Learning with Optimal Kernel and Parallel Sample Feature Selection—0
Enhancing learning in spiking neural networks through neuronal heterogeneity and neuromodulatory signaling—0
Enhancing LLM-based Recommendation through Semantic-Aligned Collaborative Knowledge—0
Enhancing Low Resource NER Using Assisting Language And Transfer Learning—0
Deep Decomposition for Stochastic Normal-Abnormal Transport—0
Enhancing Multilingual Capabilities of Large Language Models through Self-Distillation from Resource-Rich Languages—0
Enhancing Non-mass Breast Ultrasound Cancer Classification With Knowledge Transfer—0
Enhancing Performance, Calibration Time and Efficiency in Brain-Machine Interfaces through Transfer Learning and Wearable EEG Technology—0
Enhancing Polynomial Chaos Expansion Based Surrogate Modeling using a Novel Probabilistic Transfer Learning Strategy—0
Enhancing Pothole Detection and Characterization: Integrated Segmentation and Depth Estimation in Road Anomaly Systems—0
Automatic detection of rare pathologies in fundus photographs using few-shot learning—0
Enhancing radioisotope identification in gamma spectra via supervised domain adaptation—0
Classification of All Blood Cell Images using ML and DL Models—0
Enhancing Skin Disease Classification Leveraging Transformer-based Deep Learning Architectures and Explainable AI—0
A Multi-Task and Multi-Label Classification Model for Implicit Discourse Relation Recognition—0
Deep Cross Networks with Aesthetic Preference for Cross-domain Recommendation—0
Classification of breast cancer histology images using transfer learning—0
Enhancing the Authenticity of Rendered Portraits with Identity-Consistent Transfer Learning—0
Automatic Detection of COVID-19 and Pneumonia from Chest X-Ray using Deep 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