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 4176–4200 of 10307 papers

TitleStatusHype
Enhancing Non-mass Breast Ultrasound Cancer Classification With Knowledge Transfer—0
Applying Transfer Learning for Improving Domain-Specific Search Experience Using Query to Question Similarity—0
Enhancing Multilingual Capabilities of Large Language Models through Self-Distillation from Resource-Rich Languages—0
Enhancing Machine Learning Potentials through Transfer Learning across Chemical Elements—0
Enhancing Low Resource NER Using Assisting Language And Transfer Learning—0
Enhancing LLM-based Recommendation through Semantic-Aligned Collaborative Knowledge—0
Classification of Alzheimer's Disease Using the Convolutional Neural Network (CNN) with Transfer Learning and Weighted Loss—0
Applying Knowledge Transfer for Water Body Segmentation in Peru—0
Adversarial Teacher-Student Learning for Unsupervised Domain Adaptation—0
Enhancing learning in spiking neural networks through neuronal heterogeneity and neuromodulatory signaling—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 Instance-Level Image Classification with Set-Level Labels—0
Enhancing Industrial Transfer Learning with Style Filter: Cost Reduction and Defect-Focus—0
CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks—0
Applying Deep Neural Networks to automate visual verification of manual bracket installations in aerospace—0
Classical-to-Quantum Transfer Learning for Spoken Command Recognition Based on Quantum Neural Networks—0
Two-Level Adversarial Visual-Semantic Coupling for Generalized Zero-shot Learning—0
Classical-to-quantum convolutional neural network transfer learning—0
Applied Computer Vision on 2-Dimensional Lung X-Ray Images for Assisted Medical Diagnosis of Pneumonia—0
Active Learning Approaches to Enhancing Neural Machine Translation—0
Accelerating hydrodynamic simulations of urban drainage systems with physics-guided machine learning—0
Enhancing Few-Shot Transfer Learning with Optimized Multi-Task Prompt Tuning through Modular Prompt Composition—0
Enhancing Entertainment Translation for Indian Languages using Adaptive Context, Style and LLMs—0
Class dependency based learning using Bi-LSTM coupled with the transfer learning of VGG16 for the diagnosis of Tuberculosis from chest x-rays—0
Enhancing ensemble learning and transfer learning in multimodal data analysis by adaptive dimensionality reduction—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