Improving fine-tuning of self-supervised models with Contrastive Initialization

Pan, Haolin; Guo, Yong; Deng, Qinyi; Yang, Huanming; Chen, Jian; Chen, Yiqun

doi:10.1016/j.neunet.2022.12.012

Cited by 13 publications

(1 citation statement)

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“…Data augmentation, also known as Data enhancement, is a technique for increasing the quantity and diversity of limited data. Unlike other common methods to prevent overfitting, such as pretraining [14], dropout [15], batch normalization [16], and transfer learning [17], data augmentation methods start from the root cause of the overfitting problem, i.e., insufficient training samples, and prevent overfitting by simulating the possible changes in real data through mathematical simulation. As shown in Figure 1, it can extract more generalized information and features from small data sets, increase the size of the data set used to train machine learning models, and finally enhance the precision and generalizability of those models [18].…”

Section: Introductionmentioning

confidence: 99%

Review of Image Augmentation Used in Deep Learning-Based Material Microscopic Image Segmentation

Zhou

et al. 2023

Applied Sciences

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The deep learning-based image segmentation approach has evolved into the mainstream of target detection and shape characterization in microscopic image analysis. However, the accuracy and generalizability of deep learning approaches are still hindered by the insufficient data problem that results from the high expense of human and material resources for microscopic image acquisition and annotation. Generally, image augmentation can increase the amount of data in a short time by means of mathematical simulation, and has become a necessary module for deep learning-based material microscopic image analysis. In this work, we first review the commonly used image augmentation methods and divide more than 60 basic image augmentation methods into eleven categories based on different implementation strategies. Secondly, we conduct experiments to verify the effectiveness of various basic image augmentation methods for the image segmentation task of two classical material microscopic images using evaluation metrics with different applicabilities. The U-Net model was selected as a representative benchmark model for image segmentation tasks, as it is the classic and most widely used model in this field. We utilize this model to verify the improvement of segmentation performance by various augmentation methods. Then, we discuss the advantages and applicability of various image augmentation methods in the material microscopic image segmentation task. The evaluation experiments and conclusions in this work can serve as a guide for the creation of intelligent modeling frameworks in the materials industry.

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Section: Introductionmentioning

confidence: 99%