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多元图像复原(计算机视觉)Pluralistic Image Completion
Abstract
Most image completion methods produce only one result
for each masked input, although there may be many reasonable possibilities. In this paper, we present an approach for pluralistic image completion – the task of generating multiple and diverse plausible solutions for image completion.
A major challenge faced by learning-based approaches is that usually only one ground truth training instance per label. As such, sampling from conditional VAEs still leads
to minimal diversity. To overcome this, we propose a novel and probabilistically principled framework with two parallel paths. One is a reconstructive path that utilizes the only
one given ground truth to get prior distribution of missing parts and rebuild the original image from this distribution. The other is a generative path for which the conditional
prior is coupled to the distribution obtained in the reconstructive path. Both are supported by GANs. We also introduce a new short+long term attention layer that exploits
distant relations among decoder and encoder features, improving appearance consistency. When tested on datasets with buildings (Paris), faces (CelebA-HQ), and natural images (ImageNet), our method not only generated higherquality completion results, but also with multiple and diverse plausible outputs.
2020-03-11
超级缩放解析内核Blind Super-Resolution Kernel Estimation using an Internal-GAN
Abstract
Super resolution (SR) methods typically assume that the low-resolution (LR) image
was downscaled from the unknown high-resolution (HR) image by a fixed ‘ideal’
downscaling kernel (e.g. Bicubic downscaling). However, this is rarely the case
in real LR images, in contrast to synthetically generated SR datasets. When
the assumed downscaling kernel deviates from the true one, the performance of
SR methods significantly deteriorates. This gave rise to Blind-SR – namely, SR
when the downscaling kernel (“SR-kernel”) is unknown. It was further shown
that the true SR-kernel is the one that maximizes the recurrence of patches across
scales of the LR image. In this paper we show how this powerful cross-scale
recurrence property can be realized using Deep Internal Learning. We introduce
“KernelGAN”, an image-specific Internal-GAN [29], which trains solely on the LR
test image at test time, and learns its internal distribution of patches. Its Generator
is trained to produce a downscaled version of the LR test image, such that its
Discriminator cannot distinguish between the patch distribution of the downscaled
image, and the patch distribution of the original LR image. The Generator, once
trained, constitutes the downscaling operation with the correct image-specific
SR-kernel. KernelGAN is fully unsupervised, requires no training data other than
the input image itself, and leads to state-of-the-art results in Blind-SR when plugged
into existing SR algorithms. 1
2020-03-11
分析提高图像质量计算机视觉Analyzing and Improving the Image Quality of StyleGAN
Abstract
The style-based GAN architecture (StyleGAN) yields
state-of-the-art results in data-driven unconditional generative image modeling. We expose and analyze several of its
characteristic artifacts, and propose changes in both model
architecture and training methods to address them. In particular, we redesign generator normalization, revisit progressive growing, and regularize the generator to encourage good conditioning in the mapping from latent vectors
to images. In addition to improving image quality, this path
length regularizer yields the additional benefit that the generator becomes significantly easier to invert. This makes it
possible to reliably detect if an image is generated by a particular network. We furthermore visualize how well the generator utilizes its output resolution, and identify a capacity
problem, motivating us to train larger models for additional
quality improvements. Overall, our improved model rede-
fines the state of the art in unconditional image modeling,
both in terms of existing distribution quality metrics as well
as perceived image quality.
2020-03-11
什么是人工智能 WHAT IS ARTIFICIAL INTELLIGENCE
以采访的形式展现了一个斯坦福教授John McCarthy对未来人工智能的各种看法,如今看来可以被称为预言
2020-03-11
价值收益与优化-菲利普斯 pricing and revenue optimization
This book grew out of courses in pricing and revenue optimization developed at Columbia
University and Stanford University. 1 At the time there were few other comparable courses. 2
Since then, it has become clear that there is growing interest in pricing and revenue opti-
mization (a.k.a. revenue management and dynamic pricing) as a topic of study within both
business schools and management science/operations research departments. This interest
is quite understandable: Not only is pricing and revenue optimization an important appli-
cations arena for quantitative analysis, it has achieved widely publicized successes in many
industries, and there is growing interest in the techniques of pricing and revenue optimiza-
tion across many different industries. Some of the issues involved in developing and teach-
ing an MBA course in pricing and revenue optimization have been treated in articles by
Peter Bell (2004) and myself (Phillips 2004).
2019-01-19
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