You are all cordially invited to the AMLab seminar on Thursday November 8 at 16:00 in C3.163, where Wendy Shang will give a talk titled “Channel-Recurrent Autoencoding”. Afterwards there are the usual drinks and snacks!
Abstract: Understanding the functionalities of high-level features from deep neural networks (DNNs) is a long standing challenge. Towards achieving this ultimate goal, we propose a channel-recurrent architecture in place of the vanilla fully-connected layers to construct more interpretable and expressive latent spaces. Building on Variational Autoencoders (VAEs), we integrate recurrent connections across channels to both inference and generation steps, allowing the high-level features to be captured in global-to-local, coarse-to-fine manners. Combined with adversarial loss as well as two novel regularizations–namely the KL objective weighting scheme over time steps and mutual information maximization between transformed latent variables and the outputs, our channel-recurrent VAE-GAN (crVAE-GAN) outperforms VAE-GAN in generating a diverse spectrum of high resolution images while maintaining the same level of computational efficacy. Moreover, when applying crVAE-GAN in an attribute-conditioned generative setup, we further augment an attention mechanism over each attribute to indicate the specific latent subset responsible for its modulation, further imposing semantic meanings to the latent spaces. Evaluations are through both qualitative visual examination and quantitative metrics.