17 research outputs found
Dynamically Grown Generative Adversarial Networks
Recent work introduced progressive network growing as a promising way to ease
the training for large GANs, but the model design and architecture-growing
strategy still remain under-explored and needs manual design for different
image data. In this paper, we propose a method to dynamically grow a GAN during
training, optimizing the network architecture and its parameters together with
automation. The method embeds architecture search techniques as an interleaving
step with gradient-based training to periodically seek the optimal
architecture-growing strategy for the generator and discriminator. It enjoys
the benefits of both eased training because of progressive growing and improved
performance because of broader architecture design space. Experimental results
demonstrate new state-of-the-art of image generation. Observations in the
search procedure also provide constructive insights into the GAN model design
such as generator-discriminator balance and convolutional layer choices.Comment: Accepted to AAAI 202
Attending Category Disentangled Global Context for Image Classification
In this paper, we propose a general framework for image classification using
the attention mechanism and global context, which could incorporate with
various network architectures to improve their performance. To investigate the
capability of the global context, we compare four mathematical models and
observe the global context encoded in the category disentangled conditional
generative model could give more guidance as "know what is task irrelevant will
also know what is relevant". Based on this observation, we define a novel
Category Disentangled Global Context (CDGC) and devise a deep network to obtain
it. By attending CDGC, the baseline networks could identify the objects of
interest more accurately, thus improving the performance. We apply the
framework to many different network architectures and compare with the
state-of-the-art on four publicly available datasets. Extensive results
validate the effectiveness and superiority of our approach. Code will be made
public upon paper acceptance.Comment: Under revie