3 research outputs found
Towards Evolutional Compression
Compressing convolutional neural networks (CNNs) is essential for
transferring the success of CNNs to a wide variety of applications to mobile
devices. In contrast to directly recognizing subtle weights or filters as
redundant in a given CNN, this paper presents an evolutionary method to
automatically eliminate redundant convolution filters. We represent each
compressed network as a binary individual of specific fitness. Then, the
population is upgraded at each evolutionary iteration using genetic operations.
As a result, an extremely compact CNN is generated using the fittest
individual. In this approach, either large or small convolution filters can be
redundant, and filters in the compressed network are more distinct. In
addition, since the number of filters in each convolutional layer is reduced,
the number of filter channels and the size of feature maps are also decreased,
naturally improving both the compression and speed-up ratios. Experiments on
benchmark deep CNN models suggest the superiority of the proposed algorithm
over the state-of-the-art compression methods
Evolutionary Generative Adversarial Networks
Generative adversarial networks (GAN) have been effective for learning
generative models for real-world data. However, existing GANs (GAN and its
variants) tend to suffer from training problems such as instability and mode
collapse. In this paper, we propose a novel GAN framework called evolutionary
generative adversarial networks (E-GAN) for stable GAN training and improved
generative performance. Unlike existing GANs, which employ a pre-defined
adversarial objective function alternately training a generator and a
discriminator, we utilize different adversarial training objectives as mutation
operations and evolve a population of generators to adapt to the environment
(i.e., the discriminator). We also utilize an evaluation mechanism to measure
the quality and diversity of generated samples, such that only well-performing
generator(s) are preserved and used for further training. In this way, E-GAN
overcomes the limitations of an individual adversarial training objective and
always preserves the best offspring, contributing to progress in and the
success of GANs. Experiments on several datasets demonstrate that E-GAN
achieves convincing generative performance and reduces the training problems
inherent in existing GANs.Comment: 14 pages, 8 figure
Balanced Binary Neural Networks with Gated Residual
Binary neural networks have attracted numerous attention in recent years.
However, mainly due to the information loss stemming from the biased
binarization, how to preserve the accuracy of networks still remains a critical
issue. In this paper, we attempt to maintain the information propagated in the
forward process and propose a Balanced Binary Neural Networks with Gated
Residual (BBG for short). First, a weight balanced binarization is introduced
to maximize information entropy of binary weights, and thus the informative
binary weights can capture more information contained in the activations.
Second, for binary activations, a gated residual is further appended to
compensate their information loss during the forward process, with a slight
overhead. Both techniques can be wrapped as a generic network module that
supports various network architectures for different tasks including
classification and detection. We evaluate our BBG on image classification tasks
over CIFAR-10/100 and ImageNet and on detection task over Pascal VOC. The
experimental results show that BBG-Net performs remarkably well across various
network architectures such as VGG, ResNet and SSD with the superior performance
over state-of-the-art methods in terms of memory consumption, inference speed
and accuracy.Comment: Accepted by ICASSP202