Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A New CGAN Technique for Constrained Topology Design Optimization   This paper presents a new conditional GAN (named convex relaxing CGAN or
crCGAN) to replicate the conventional constrained topology optimization
algorithms in an extremely effective and efficient process. The proposed crCGAN
consists of a generator and a discriminator, both of which are deep
convolutional neural networks (CNN) and the topology design constraint can be
conditionally set to both the generator and discriminator. In order to improve
the training efficiency and accuracy due to the dependency between the training
images and the condition, a variety of crCGAN formulation are introduced to
relax the non-convex design space. These new formulations were evaluated and
validated via a series of comprehensive experiments. Moreover, a minibatch
discrimination technique was introduced in the crCGAN training process to
stabilize the convergence and avoid the mode collapse problems. Additional
verifications were conducted using the state-of-the-art MNIST digits and
CIFAR-10 images conditioned by class labels. The experimental evaluations
clearly reveal that the new objective formulation with the minibatch
discrimination training provides not only the accuracy but also the consistency
of the designs.

10
The categories of this paper are: q-bio
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
19
The categories of this paper are: eess
29
The categories of this paper are: funct-an
17
The categories of this paper are: q-fin
23
The categories of this paper are: solv-int
6
The categories of this paper are: astro-ph
12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas
13
The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn
14
The categories of this paper are: math-ph
7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an