Deep AutoEncoder-based Lossy Geometry Compression for Point Clouds https://arxiv.org/abs/1905.03691 Sampling layer 在G-PCC中,基于八叉树的几何编码根据量化尺度来控制有损几何压缩,设输入点云为: G-PCC编码器的量化计算如下: 其中 和 是用户人工定义的参数。 量化后,将有许多重复点共享...
Here, we propose a memristor-based storage system with an integrated near-storage in-memory computing-based convolutional autoencoder compression network to boost the energy efficiency and speed of the image compression/retrieval and improve the storage density. We adopt the 4-bit...
Variational Autoencoders (VAEs), as a form of deep generative model, have been widely used in recent years, and shown great great peformance in a number of different domains, including image generation and anomaly detection, etc.. This paper aims to explore neural network model compression meth...
The exponential growth of various complex images is putting tremendous pressure on storage systems. Here, we propose a memristor-based storage system with an integrated near-storage in-memory computing-based convolutional autoencoder compression network
With the CD-VAE approach, only continuous parameters (\(\mathbf{p}_{ \text{continuous}}\)) are processed for compression into the latent space z and subsequent reconstruction. Discrete parameters (\(\mathbf{p}_{\text{discrete}}\)), such as topology or the maximum AC line current, are ...
we propose a set of significant improvements to patch-based point cloud compression, i.e., a learnable context model for entropy coding, octree coding for sampling centroid points, and an integrated compression and training process. In addition, we propose an adversarial network to improve the uni...
Ichimoku-based features have gained significant attention in financial market analysis due to their ability to capture essential market signals and patterns. This significant compression retains essential patterns related to trends, support/resistance levels, and trading signals. The reduced dimensionality ...
Deep learning based multi-scale channel compression feature surface defect detection system 简述:首先应用背景分割和模板匹配技术来定义覆盖目标工件的ROI区域。提取的感兴趣区域被均匀地裁剪成若干个图像块,每个块被送到基于CNN的模型,以分类杂乱背景中不同大小的表面缺陷。最后,对空间上相邻且具有相同类别标签的图像...
[7]. Autoencoder is capable of capturing complex features with nonlinear dependency, which makes it an ideal candidate for accounting for sophisticated gene-gene interactions in a cell. Due to the dimension compression ability of autoencoder, DCA shares and pools information across both features (...
We introduce ADMM-pruned Compressive AutoEncoder (CAE-ADMM) that uses Alternative Direction Method of Multipliers (ADMM) to optimize the trade-off between distortion and efficiency of lossy image compression. Specifically, ADMM in our method is to promote sparsity to implicitly optimize the bitrate, ...