Lightweight CNN architectures, known for their efficiency and speed without compromising accuracy, play a crucial role in addressing the challenges posed by ...
Our study focused on evaluating a multi-stage transfer learning approach across various dataset sizes and truncated versions of the MobileNetV2 architecture.
Lastly, in this study, we aim to analyze the performance of truncated lightweight models against different training dataset sizes in a multi-stage transfer ...
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IMPACT OF DATASET SIZE AND TRANSFER LEARNING ON TRUNCATED LIGHTWEIGHT ARCHITECTURE. R Godasu, D Zeng, K Sutrave. 2024. Session 5: Robust CNN-based Automatic ...
Mirroring and Feature Fusion After truncation, the model had significantly reduced in size, leading to lesser performance when learning features. For the model ...
[PDF] Effectiveness of Transfer Learning with Light-weight Architecture for ...
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transfer learning method across several dataset sizes and truncated versions of the lightweight. MobileNetV2 architecture for medical image classification.
Mar 3, 2022 · This work investigated the effects of parameter reduction through a proposed truncation method and analyzed its effects.
Jan 15, 2021 · This study aims to investigate the impact of dataset size on the overall performance of supervised classification models.
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Apr 3, 2024 · classification type, dataset sizes, and data ... A lightweight capsule network architecture for detection of COVID-19 from lung CT scans.