70,852 research outputs found
Controlling Perceptual Factors in Neural Style Transfer
Neural Style Transfer has shown very exciting results enabling new forms of
image manipulation. Here we extend the existing method to introduce control
over spatial location, colour information and across spatial scale. We
demonstrate how this enhances the method by allowing high-resolution controlled
stylisation and helps to alleviate common failure cases such as applying ground
textures to sky regions. Furthermore, by decomposing style into these
perceptual factors we enable the combination of style information from multiple
sources to generate new, perceptually appealing styles from existing ones. We
also describe how these methods can be used to more efficiently produce large
size, high-quality stylisation. Finally we show how the introduced control
measures can be applied in recent methods for Fast Neural Style Transfer.Comment: Accepted at CVPR201
Style Transfer in Text: Exploration and Evaluation
Style transfer is an important problem in natural language processing (NLP).
However, the progress in language style transfer is lagged behind other
domains, such as computer vision, mainly because of the lack of parallel data
and principle evaluation metrics. In this paper, we propose to learn style
transfer with non-parallel data. We explore two models to achieve this goal,
and the key idea behind the proposed models is to learn separate content
representations and style representations using adversarial networks. We also
propose novel evaluation metrics which measure two aspects of style transfer:
transfer strength and content preservation. We access our models and the
evaluation metrics on two tasks: paper-news title transfer, and
positive-negative review transfer. Results show that the proposed content
preservation metric is highly correlate to human judgments, and the proposed
models are able to generate sentences with higher style transfer strength and
similar content preservation score comparing to auto-encoder.Comment: To appear in AAAI-1
Ultrafast Photorealistic Style Transfer via Neural Architecture Search
The key challenge in photorealistic style transfer is that an algorithm
should faithfully transfer the style of a reference photo to a content photo
while the generated image should look like one captured by a camera. Although
several photorealistic style transfer algorithms have been proposed, they need
to rely on post- and/or pre-processing to make the generated images look
photorealistic. If we disable the additional processing, these algorithms would
fail to produce plausible photorealistic stylization in terms of detail
preservation and photorealism. In this work, we propose an effective solution
to these issues. Our method consists of a construction step (C-step) to build a
photorealistic stylization network and a pruning step (P-step) for
acceleration. In the C-step, we propose a dense auto-encoder named PhotoNet
based on a carefully designed pre-analysis. PhotoNet integrates a feature
aggregation module (BFA) and instance normalized skip links (INSL). To generate
faithful stylization, we introduce multiple style transfer modules in the
decoder and INSLs. PhotoNet significantly outperforms existing algorithms in
terms of both efficiency and effectiveness. In the P-step, we adopt a neural
architecture search method to accelerate PhotoNet. We propose an automatic
network pruning framework in the manner of teacher-student learning for
photorealistic stylization. The network architecture named PhotoNAS resulted
from the search achieves significant acceleration over PhotoNet while keeping
the stylization effects almost intact. We conduct extensive experiments on both
image and video transfer. The results show that our method can produce
favorable results while achieving 20-30 times acceleration in comparison with
the existing state-of-the-art approaches. It is worth noting that the proposed
algorithm accomplishes better performance without any pre- or post-processing
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