We address the problem of synthesizing new video frames in an existing video,
either in-between existing frames (interpolation), or subsequent to them
(extrapolation). This problem is challenging because video appearance and
motion can be highly complex. Traditional optical-flow-based solutions often
fail where flow estimation is challenging, while newer neural-network-based
methods that hallucinate pixel values directly often produce blurry results. We
combine the advantages of these two methods by training a deep network that
learns to synthesize video frames by flowing pixel values from existing ones,
which we call deep voxel flow. Our method requires no human supervision, and
any video can be used as training data by dropping, and then learning to
predict, existing frames. The technique is efficient, and can be applied at any
video resolution. We demonstrate that our method produces results that both
quantitatively and qualitatively improve upon the state-of-the-art.

通过融合光流和深度学习的方法，我们提出了一种无需人工监督的视频帧合成技术，通过流动像素值来预测新帧，实验表明该方法在定量和定性上都优于现有技术。

使用深度体素流合成视频帧

Video Frame Synthesis using Deep Voxel Flow

Most existing star-galaxy classifiers use the reduced summary information
from catalogs, requiring careful feature extraction and selection. The latest
advances in machine learning that use deep convolutional neural networks allow
a machine to automatically learn the features directly from data, minimizing
the need for input from human experts. We present a star-galaxy classification
framework that uses deep convolutional neural networks (ConvNets) directly on
the reduced, calibrated pixel values. Using data from the Sloan Digital Sky
Survey (SDSS) and the Canada-France-Hawaii Telescope Lensing Survey (CFHTLenS),
we demonstrate that ConvNets are able to produce accurate and well-calibrated
probabilistic classifications that are competitive with conventional machine
learning techniques. Future advances in deep learning may bring more success
with current and forthcoming photometric surveys, such as the Dark Energy
Survey (DES) and the Large Synoptic Survey Telescope (LSST), because deep
neural networks require very little, manual feature engineering.

本研究提出了一种使用深度卷积神经网络的恒星 - 星系分类框架，可直接基于像素值进行学习，避免了人工特征工程需求，并且能在 SDSS 和 CFHTLenS 数据集上取得与传统机器学习技术相竞争的准确和可靠的概率分类结果。