BDARS_CapsNet: Bi-Directional Attention Routing Sausage Capsule Network

Abstract

In order to improve the accuracy of capsule network in disentangled representation, and further expand its application in computer vision, a novel BDARS_CapsNet (bi-directional attention routing sausage capsule network) architecture is proposed in this paper. Firstly, the bi-directional routing, namely bottomup and top-down attention is used to achieve information feed-forward and feedback mechanism, which contributes to describing the attributes of object entity more accurately and completely. Secondly, inspired by the concept of covering learning, the sausage measure model is introduced into the network. The sausage model measures both the similarities and differences of the capsules and projects them into a more complex curved surface, which makes it possible to approximate any nonlinear function with arbitrary precision and preserving the local responsiveness of capsule entity to the maximum. Finally, the BDARS_CapsNet combines the CNN (Convolutional Neural Network), bi-directional attention routing, and sausage measure into capsule network modeling, and makes full use of high-level category information and low-level vision information; as a result, the reconstruction and classification accuracy is accordingly improved. Experiments demonstrate the effectiveness of proposed information routing, sausage measure, and new framework. Furthermore, the proposed BDARS_CapsNet provides a foundation for future research on disentangled representation learning.

A Study of Stock Market Predictability Based on Financial Time Series Models

“…In the study of U.S. stock returns, the time series heteroskedasticity of the variance of stock returns was found by comparing the correlation of the returns of various stock indices [9]. Based on this, financial scholars began to focus on financial time series analysis and tried to introduce time series into the stock market metrics, and volatility analysis models closely related to this emerged, the most dazzling of which is the ARCH-type model proposed by the authors of [10]. In the analysis of foreign exchange market risk volatility and when analyzing the risk volatility and returns in the foreign exchange market, considering that the risk premium cannot be measured, a new model was constructed by explaining the risk premium in terms of conditional variance values, and the valuation eventually fitted well.…”

Capsule-inferenced Object Detection for Remote Sensing Images

“…To solve the above problems, inspired by the non-linear sausage measure [54], we replaced the squash activation function of CapsNet with the sausage measure, which is a measure model with locally responsive properties adopted for approximating non-linear mapping with arbitrary precision.…”

Remote Sensing Image Compression Evaluation Method Based on Neural Network Prediction and Fusion Quality Fidelity

“…Figure 2 shows the overall structure design of the network. e input image A is first passed through the designed neural network [12][13][14][15] and changed into a slightly changed image A' that is more suitable for image compression. is slightly changed image A' is input to a traditional encoding framework such as JPEG2000.…”

“…Following Fourure et al [10], we adopt a GridNet architecture, composed of a two-dimensional grid pattern shown in Figure 3. Maps inside the model are connected via computational layers [12,14]. Data are input to the model in the first block (line 0), and output in the last block.…”