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Generative Models for General Audiences

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Sangwoo Mo
Sangwoo Mo

Introduction (application) of generative models for general audiences. Many figures are borrowed from https://lilianweng.github.io.

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Generation: Deep Generative Models
What is Generative Model? • Generative model learns the distribution of data without label • Create new data & Modify existing data • Image/video/language/speech generation • Data augmentation & semi-supervised learning • Data privacy (e.g., public release of medical dataset)
What is Generative Model? • Generative model learns the distribution of data without label • Unsupervised representation learning • Learning “good” representation with unlabeled data • Design an auxiliary task (hence often called self-supervised learning) • Generative model is a popular approach for unsupervised learning
Major Breakthroughs in Deep Generative Models 1980 1985 1990 2000 2005 2010 2015 1985 2006 Boltzmann machine (1985) • By G. Hinton et al. • Undirected graphical model • Computationally expensive Helmholtz machine (1986) • Directed graphical model Contrastivedivergence(1989) • G. Hinton et.al • Easy method for training RBM 1986 Deep Boltzmann machine (2009) • Undirected deep generative model consists of stacks of RBM • Layerwise training followed by joint learning Restricted Boltzmann machine (1986) • Bipartite version of BM 1995 Variational Autoencoder (2013) • By Durk Kingma et al. • Easy NN like back-propagation learning in deep generative model Greedilylayer-wisepre-training(2006) • Deep Belief Networks • Major breakthrough in learning deep generative model Generative Adversarial Network (2014) • Large scale image generative model G. Hinton, S. Ruslan D. Kingma, M. Welling I. GoodfellowG. Hinton, T. Sejnowski P. Smolensky G. Hinton, R. Neal • Hierarchical feature learning• Restricted Boltzmann Machine • Contrastive Divergence • Variatianal Autoencoder 2002 2009 2013 2014 2015 Ladder Network (2015) • Performance breakthrough in Semi-supervised learning
Approaches for Generative Models 1. Flow-based (autoregressive) model • Pros: exactly compute the probability of the data (many applications) • Cons: slow inference (autoregressive) or low quality (non-autoregressive) Autoregressive (e.g., PixelCNN) Non-autoregressive (e.g., Normalizing Flow)
Approaches for Generative Models 2. Variational autoencoder (VAE) • Pros: stable training & theoretical properties (lower bound of likelihood) • Cons: known to produce blurry outputs1 1. Recent methods combine VAE and other methods, e.g., IAF-VAE (+ flow) or WAE (+ GAN) to improve the performance Blurry!
Approaches for Generative Models 3. Generative adversarial network (GAN) • Pros: good performance (most SOTA models are based on GAN) • Cons: hard to train (alternating two networks leads instability)
Application: Image Generation • BigGAN
Application: Image Generation • StyleGAN (https://www.youtube.com/watch?v=kSLJriaOumA)
Application: Image-to-Image Translation • pix2pix (paired)
Application: Image-to-Image Translation • CycleGAN (unpaired)
Application: Image-to-Image Translation • StarGAN (multi-domain)
Application: Image-to-Image Translation • MUNIT (diverse output)
Application: Image-to-Image Translation • InstaGAN (shape modification) (from our lab)
Application: Emoji Generation • DTN (create personal avatar)
Application: Semantic Manipulation • pix2pixHD (https://www.youtube.com/watch?v=3AIpPlzM_qs)
Application: Pose Guided Generation • PG2 (change pose of person)
Application: Cloth Extraction • PixelDTGAN (extract cloth from image)
Application: Text-to-Image Synthesis • Reed et al.
Application: Text-to-Image Synthesis • Hong et al. (control location with bounding box)
Application: Video-to-Video Translation • vid2vid (paired) (https://www.youtube.com/watch?v=HCqXJth9t_k)
Application: Video-to-Video Translation • everybody dance now (with pose) (https://www.youtube.com/watch?v=PCBTZh41Ris)
Application: Video-to-Video Translation • Recycle-GAN (unpaired) (https://www.youtube.com/watch?v=F51RCdDIuUw)
Application: Data Augmentation • DAGAN (augment data to improve neural network performance)
Application: Anomaly Detection • AnoGAN (find anomaly from given data)

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Generative Models for General Audiences