论文标题
剩余回程自动编码器用于形状插图图像
Residual-Recursion Autoencoder for Shape Illustration Images
论文作者
论文摘要
形状插图图像（SII）对于描述工业产品的横截面很常见且重要。与MNIST相同，手写的数字图像，SII是灰色或二进制的，包含被大面积空白所包围的形状。在这项工作中，已经提出了剩余回归自动编码器（RRAE）来从SII中提取低维特征，同时保持重建精度尽可能高。 RRAE将尝试多次重建原始图像，并在下一次重建试验之前递归地填充最新的残留图像到编码器输入的保留通道。作为一种神经网络培训框架，RRAE可以将其他自动编码器包裹起来并提高其性能。从实验结果中，具有高分辨率SIIS的卷积自动编码器的重建损失减少了86.47％，变异自动编码器的重建损失为10.77％，与MNIST的条件变异自动编码器为8.06％。
Shape illustration images (SIIs) are common and important in describing the cross-sections of industrial products. Same as MNIST, the handwritten digit images, SIIs are gray or binary and containing shapes that are surrounded by large areas of blanks. In this work, Residual-Recursion Autoencoder (RRAE) has been proposed to extract low-dimensional features from SIIs while maintaining reconstruction accuracy as high as possible. RRAE will try to reconstruct the original image several times and recursively fill the latest residual image to the reserved channel of the encoder's input before the next trial of reconstruction. As a kind of neural network training framework, RRAE can wrap over other autoencoders and increase their performance. From experiment results, the reconstruction loss is decreased by 86.47% for convolutional autoencoder with high-resolution SIIs, 10.77% for variational autoencoder and 8.06% for conditional variational autoencoder with MNIST.
