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Title: Likelihood Contribution based Multi-scale Architecture for Generative Flows

Paper content:
Deep generative modeling using flows has gained popularity owing to the tractable exact log-likelihood estimation with efficient training and synthesis process.
However, flow models suffer from the challenge of having high dimensional latent space, same in dimension as the input space.
An effective solution to the above challenge as proposed by Dinh et al. (2016) is a multi-scale architecture, which is based on iterative early factorization of a part of the total dimensions at regular intervals.
Prior works on generative flows involving a multi-scale architecture perform the dimension factorization based on a static masking.
We propose a novel multi-scale architecture that performs data dependent factorization to decide which dimensions should pass through more flow layers.
To facilitate the same, we introduce a heuristic based on the contribution of each dimension to the total log-likelihood which encodes the importance of the dimensions.
Our proposed heuristic is readily obtained as part of the flow training process, enabling versatile implementation of our likelihood contribution based multi-scale architecture for generic flow models.
We present such an implementation for the original flow introduced in Dinh et al. (2016), and demonstrate improvements in log-likelihood score and sampling quality on standard image benchmarks.
We also conduct ablation studies to compare proposed method with other options for dimension factorization.
Deep Generative Modeling aims to learn the embedded distributions and representations in input (especially unlabelled) data, requiring no/minimal human labelling effort.
Learning without knowledge of labels (unsupervised learning) is of increasing importance because of the abundance of unlabelled data and the rich inherent patterns they posses.
The representations learnt can then be utilized in a number of downstream tasks such as semi-supervised learning Odena, 2016) , synthetic data augmentation and adversarial training (Cisse et al., 2017) , text analysis and model based control etc.
The repository of deep generative modeling majorly includes Likelihood based models such as autoregressive models (Oord et al., 2016b; Graves, 2013) , latent variable models (Kingma & Welling, 2013) , flow based models (Dinh et al., 2014; 2016; Kingma & Dhariwal, 2018) and implicit models such as generative adversarial networks (GANs) (Goodfellow et al., 2014) .
Autoregressive models (Salimans et al., 2017; Oord et al., 2016b; a; achieve exceptional log-likelihood score on many standard datasets, indicative of their power to model the inherent distribution.
But, they suffer from slow sampling process, making them unacceptable to adopt in real world applications.
Latent variable models such as variational autoencoders (Kingma & Welling, 2013) tend to better capture the global feature representation in data, but do not offer an exact density estimate.
Implicit generative models such as GANs which optimize a generator and a discriminator in a min-max fashion have recently become popular for their ability to synthesize realistic data (Karras et al., 2018; Engel et al., 2019) .
But, GANs do not offer a latent space suitable for further downstream tasks, nor do they perform density estimation.
Flow based generative models (Dinh et al., 2016; Kingma & Dhariwal, 2018) perform exact density estimation with fast inference and sampling, due to their parallelizability.
They also provide an information rich latent space suitable for many applications.
However, the dimension of latent space for flow based generative models is same as the high-dimensional input space, by virtue of bijectivity nature of flows.
This poses a bottleneck for flow models to scale with increasing input dimensions due to computational complexity.
An effective solution to the above challenge is a multi-scale architecture, introduced by Dinh et al. (2016) , which performs iterative early gaussianization of a part of the total dimensions at regular intervals of flow layers.
This not only makes the model computational and memory efficient but also aids in distributing the loss function throughout the network for better training.
Many prior works including Kingma & Dhariwal (2018) ; Atanov et al. (2019) ; Durkan et al. (2019) ; implement multi-scale architecture in their flow models, but use static masking methods for factorization of dimensions.
We propose a multi-scale architecture which performs data dependent factorization to decide which dimensions should pass through more flow layers.
For the decision making, we introduce a heuristic based on the amount of total log-likelihood contributed by each dimension, which in turn signifies their individual importance.
We lay the ground rules for quantitative estimation and qualitative sampling to be satisfied by an ideal factorization method for a multi-scale architecture.
Since in the proposed architecture, the heuristic is obtained as part of the flow training process, it can be universally applied to generic flow models.
We present such implementations for flow models based on affine/additive coupling and ordinary differential equation (ODE) and achieve quantitative and qualitative improvements.
We also perform ablation studies to confirm the novelty of our method.
Summing up, the contributions of our research are,
We proposed a novel multi-scale architecture for generative flows which employs a data-dependent splitting based the individual contribution of dimensions to the total log-likelihood.
Implementations of the proposed method for several state-of-the-art flow models such as RealNVP (Dinh et al., 2016) , Glow(Kingma & Dhariwal, 2018) and i-ResNet (Behrmann et al., 2018) were presented.
Empirical studies conducted on benchmark image datasets validate the strength of our proposed method, which improves log-likelihood scores and is able to generate qualitative samples.
Ablation study results confirm the power of LCMA over other options for dimension factorization.

assistant: text: Data-dependent factorization of dimensions in a multi-scale architecture based on contribution to the total log-likelihood