Pre-rotation only at inference-time: a way to rotation invariance

Abstract

Weight sharing across different locations makes Convolutional Neural Networks (CNNs) space shift invariant, i.e., the weights learned in one location can be applied to recognize objects in other locations. However, weight sharing mechanism has been lacked in Rotated Pattern Recognition (RPR) tasks, and CNNs have to learn training samples in different orientations by rote. As such rote-learning strategy has greatly increased the difficulty of training, a new solution for RPR tasks, Pre-Rotation Only At Inference time (PROAI), is proposed to provide CNNs with rotation invariance. The core idea of PROAI is to share CNN weights across multiple rotated versions of the test sample. At the training time, a CNN was trained with samples only in one angle; at the inference-time, test samples were pre-rotated at different angles and then fed into the CNN to calculate classification confidences; at the end both the category and the orientation were predicted using the position of the max value of these confidences. By adopting PROAI, the recognition ability learned at one orientation can be generalized to patterns at any other orientation, and both the number of parameters and the training time of CNN in RPR tasks can be greatly reduced. Experiments show that PROAI enables CNNs with less parameters and training time to achieve state-of-the-art classification and orientation performance on both rotated MNIST and rotated Fashion MNIST datasets.

Rotated target recognition of sonar images via convolutional neural networks with rotated inputs

“…The increase of parameters will rapidly increase the size of CNN deployment files, which is not conducive to the actual deployment of CNN. In order to overcome the challenges of data augmentation, this paper improves a novel rotation pattern recognition method RICNN 18 and applies it to rotation target recognition task of actual sonar images.…”

“…The category and orientation of each target were simultaneously estimated by finding the max of these classification scores. The original RICNN only conducted experiments on MNIST and fashion MNIST datasets [18][19][20][21] , which have small image size, large sample number and balanced categories. However, underwater target sonar images usually have large image size, small sample number and unbalanced categories.…”