In this paper, we provide a detailed description on our approach designed for
CVPR 2019 Workshop and Challenge on Learned Image Compression (CLIC). Our
approach mainly consists of two proposals, i.e. deep residual learning for
image compression and sub-pixel convolution as up-sampling operations.
Experimental results have indicated that our approaches, Kattolab, Kattolabv2
and KattolabSSIM, achieve 0.972 in MS-SSIM at the rate constraint of 0.15bpp
with moderate complexity during the validation phase.

本研究旨在提高图像压缩效率，并使用深层残差学习网络和亚像素卷积等方法进行探索，最终实验结果显示 Kattolab、Kattolabv2 和 KattolabSSIM 在验证阶段下以合理的复杂度实现了 0.15bpp 下 0.972 的 MS-SSIM 得分。

深度残差学习用于图像压缩

Deep Residual Learning for Image Compression

We explore the application of deep residual learning and dilated convolutions
to the keyword spotting task, using the recently-released Google Speech
Commands Dataset as our benchmark. Our best residual network (ResNet)
implementation significantly outperforms Google's previous convolutional neural
networks in terms of accuracy. By varying model depth and width, we can achieve
compact models that also outperform previous small-footprint variants. To our
knowledge, we are the first to examine these approaches for keyword spotting,
and our results establish an open-source state-of-the-art reference to support
the development of future speech-based interfaces.

本研究采用深度残差学习和扩张卷积探索了关键词检测任务的应用，使用 Google Speech Commands 数据集作为基准。我们的最佳残差网络实现在准确性方面明显优于 Google 以前的卷积神经网络。通編变化模型深度和宽度，我们可以实现比以前的小型脚本变体更优秀的紧凑型模型。据我们所知，我们是第一个研究这些方法的关键词检测任务的人，我们的结果建立了一个开源的最新参考来支持未来基于语音的接口的开发。

小型关键词检测的深度残差学习

Deep Residual Learning for Small-Footprint Keyword Spotting

Recently deep residual learning with residual units for training very deep
neural networks advanced the state-of-the-art performance on 2D image
recognition tasks, e.g., object detection and segmentation. However, how to
fully leverage contextual representations for recognition tasks from volumetric
data has not been well studied, especially in the field of medical image
computing, where a majority of image modalities are in volumetric format. In
this paper we explore the deep residual learning on the task of volumetric
brain segmentation. There are at least two main contributions in our work.
First, we propose a deep voxelwise residual network, referred as VoxResNet,
which borrows the spirit of deep residual learning in 2D image recognition
tasks, and is extended into a 3D variant for handling volumetric data. Second,
an auto-context version of VoxResNet is proposed by seamlessly integrating the
low-level image appearance features, implicit shape information and high-level
context together for further improving the volumetric segmentation performance.
Extensive experiments on the challenging benchmark of brain segmentation from
magnetic resonance (MR) images corroborated the efficacy of our proposed method
in dealing with volumetric data. We believe this work unravels the potential of
3D deep learning to advance the recognition performance on volumetric image
segmentation.

本文介绍了在体积数据上使用基于深度残差学习的深度卷积网络 VoxResNet 用于脑部图像分割的任务，同时提出了一种通过无缝集成低阶图像外观特征、隐式形状信息和高级上下文的自动上下文版本 VoxResNet 来进一步提高体积分割性能的方法，实验证明该方法在处理体积数据方面表现出色，揭示出 3D 深度学习在体积图像分割中的潜力。