Research on data augmentation for image classification based on convolution neural networks

By Shijie Jia, Wang Ping, Jia Peiyi, Siping Hu, Siping Hu. 2017

259 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Industrial and Manufacturing Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Halil Ibrahim Aysel
2. Xiaohao Cai
3. Khaled Alomar
4. Baljit Singh Saini
5. Cherry Khosla

Citations in the corpus

• ScaleNet: An Unsupervised Representation Learning Method for Limited Information (2023, neutral): “Similar to other data augmentation methods such as flipping and cropping [43], rotating images hinder the model from learning impractical low-level visual features that are trivial information for downstream tasks [14].”
• Data Augmentation in Classification and Segmentation: A Survey and New Strategies (2023, methodology): “, 2017 [34] CIFAR10; ImageNet (10 categories) GAN/WGAN, flipping, cropping, shifting, PCA jittering, colour jittering, noise, rotation.”
• Enhancing Performance of Deep Learning Models with different Data Augmentation Techniques: A Survey (2020, background): “flip [12].”