Abstract Art is an immensely popular, discussed form of art that often has
the ability to depict the emotions of an artist. Many researchers have made
attempts to study abstract art in the form of edge detection, brush stroke and
emotion recognition algorithms using machine and deep learning. This papers
describes the study of a wide distribution of abstract paintings using
Generative Adversarial Neural Networks(GAN). GANs have the ability to learn and
reproduce a distribution enabling researchers and scientists to effectively
explore and study the generated image space. However, the challenge lies in
developing an efficient GAN architecture that overcomes common training
pitfalls. This paper addresses this challenge by introducing a modified-DCGAN
(mDCGAN) specifically designed for high-quality artwork generation. The
approach involves a thorough exploration of the modifications made, delving
into the intricate workings of DCGANs, optimisation techniques, and
regularisation methods aimed at improving stability and realism in art
generation enabling effective study of generated patterns. The proposed mDCGAN
incorporates meticulous adjustments in layer configurations and architectural
choices, offering tailored solutions to the unique demands of art generation
while effectively combating issues like mode collapse and gradient vanishing.
Further this paper explores the generated latent space by performing random
walks to understand vector relationships between brush strokes and colours in
the abstract art space and a statistical analysis of unstable outputs after a
certain period of GAN training and compare its significant difference. These
findings validate the effectiveness of the proposed approach, emphasising its
potential to revolutionise the field of digital art generation and digital art
ecosystem.

通过改进的 mDCGAN 架构、优化技术和规范化方法来改进稳定性和真实感，有效研究生成的抽象艺术样式，进一步探索随机游走的潜在空间，验证了这种方法的有效性，强调其为数字艺术生成和数字艺术生态系统领域带来的潜力。

使用改进的深度卷积生成对抗网络识别抽象艺术中的色彩和笔触模式

Colour and Brush Stroke Pattern Recognition in Abstract Art using  Modified Deep Convolutional Generative Adversarial Networks

We propose a novel method for generating abstract art. First an autoencoder
is trained to encode and decode the style representations of images, which are
extracted from source images with a pretrained VGG network. Then, the decoder
component of the autoencoder is extracted and used as a generator in a GAN. The
generator works with an ensemble of discriminators. Each discriminator takes
different style representations of the same images, and the generator is
trained to create images that create convincing style representations in order
to deceive all of the generators. The generator is also trained to maximize a
diversity term. The resulting images had a surreal, geometric quality. We call
our approach ARTEMIS (ARTistic Encoder- Multi- Discriminators Including
Self-Attention), as it uses the self-attention layers and an encoder-decoder
architecture.

我们提出了一种新颖的生成抽象艺术的方法，通过训练自动编码器，提取源图像的风格表示，并使用生成对抗网络中的解码器作为生成器，结合多个鉴别器使生成器能够欺骗全部鉴别器，并最大化多样性项，最终生成具有超现实几何特性的图像。我们将其称为 ARTEMIS（艺术编码器 - 包括自注意力的多鉴别器），因为它使用了自注意力层和编码器 - 解码器架构。

ARTEMIS: 使用多个判别器的 GAN 生成艺术

ARTEMIS: Using GANs with Multiple Discriminators to Generate Art

Although the human visual system is surprisingly robust to extreme distortion
when recognizing objects, most evaluations of computer object detection methods
focus only on robustness to natural form deformations such as people's pose
changes. To determine whether algorithms truly mirror the flexibility of human
vision, they must be compared against human vision at its limits. For example,
in Cubist abstract art, painted objects are distorted by object fragmentation
and part-reorganization, to the point that human vision often fails to
recognize them. In this paper, we evaluate existing object detection methods on
these abstract renditions of objects, comparing human annotators to four
state-of-the-art object detectors on a corpus of Picasso paintings. Our results
demonstrate that while human perception significantly outperforms current
methods, human perception and part-based models exhibit a similarly graceful
degradation in object detection performance as the objects become increasingly
abstract and fragmented, corroborating the theory of part-based object
representation in the brain.

本文通过对毕加索的绘画作品中的抽象畸变物件的检测，比较了四种目标检测算法与人眼视觉感知的相似度，结果证明人类视觉感知能远胜目前算法，并且人类视觉感知和基于部件的模型表现了类似的性能下降，印证了大脑中基于部分的目标表征理论。