Image-to-image (I2I) translation methods based on generative adversarial
networks (GANs) typically suffer from overfitting when limited training data is
available. In this work, we propose a data augmentation method (ReMix) to
tackle this issue. We interpolate training samples at the feature level and
propose a novel content loss based on the perceptual relations among samples.
The generator learns to translate the in-between samples rather than memorizing
the training set, and thereby forces the discriminator to generalize. The
proposed approach effectively reduces the ambiguity of generation and renders
content-preserving results. The ReMix method can be easily incorporated into
existing GAN models with minor modifications. Experimental results on numerous
tasks demonstrate that GAN models equipped with the ReMix method achieve
significant improvements.

本文针对生成对抗网络的图像到图像转换领域中，数据有限导致过拟合的问题，提出了一种数据增强方法（ReMix）。该方法通过对特征进行插值，并结合基于感性关系的内容损失函数，实现了通过合成训练数据来强制鉴别器泛化以降低过拟合的目的。在多项实验中，GAN 模型结合 ReMix 方法的性能得到了显著的提升。

ReMix: 有限数据下的图像到图像翻译

ReMix: Towards Image-to-Image Translation with Limited Data

Arbitrary-shaped text detection is a challenging task due to the complex
geometric layouts of texts such as large aspect ratios, various scales, random
rotations and curve shapes. Most state-of-the-art methods solve this problem
from bottom-up perspectives, seeking to model a text instance of complex
geometric layouts with simple local units (e.g., local boxes or pixels) and
generate detections with heuristic post-processings. In this work, we propose
an arbitrary-shaped text detection method, namely TextRay, which conducts
top-down contour-based geometric modeling and geometric parameter learning
within a single-shot anchor-free framework. The geometric modeling is carried
out under polar system with a bidirectional mapping scheme between shape space
and parameter space, encoding complex geometric layouts into unified
representations. For effective learning of the representations, we design a
central-weighted training strategy and a content loss which builds propagation
paths between geometric encodings and visual content. TextRay outputs simple
polygon detections at one pass with only one NMS post-processing. Experiments
on several benchmark datasets demonstrate the effectiveness of the proposed
approach. The code is available at this https URL

该研究提出了一种名为 TextRay 的任意形状文本检测方法，采用一次性无锚点框架中的自顶向下轮廓基准几何建模和几何参数学习，能够将复杂的几何布局编码为统一的表示，并输出仅经过一个 NMS 后处理的简单多边形检测。

TextRay: 基于轮廓的几何模型在任意形状场景文本检测中的应用

TextRay: Contour-based Geometric Modeling for Arbitrary-shaped Scene  Text Detection

In this paper, we propose a deep generative adversarial network for
super-resolution considering the trade-off between perception and distortion.
Based on good performance of a recently developed model for super-resolution,
i.e., deep residual network using enhanced upscale modules (EUSR), the proposed
model is trained to improve perceptual performance with only slight increase of
distortion. For this purpose, together with the conventional content loss,
i.e., reconstruction loss such as L1 or L2, we consider additional losses in
the training phase, which are the discrete cosine transform coefficients loss
and differential content loss. These consider perceptual part in the content
loss, i.e., consideration of proper high frequency components is helpful for
the trade-off problem in super-resolution. The experimental results show that
our proposed model has good performance for both perception and distortion, and
is effective in perceptual super-resolution applications.

本论文提出了一种深度生成对抗网络用于超分辨率，考虑了感知与失真之间的权衡，采用传统内容损失的基础上，使用离散余弦变换系数损失和差分内容损失来考虑内容感知，实现高分辨率图像的恢复。实验结果表明该模型在恢复图像的视觉效果和失真程度方面具有良好的性能，是一种有效的感知超分辨率应用算法。