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Title: Generative Restricted Kernel Machines

Paper content:
We introduce a novel framework for generative models based on Restricted Kernel Machines (RKMs) with multi-view generation and uncorrelated feature learning capabilities, called Gen-RKM.
To incorporate multi-view generation, this mechanism uses a shared representation of data from various views.
The mechanism is flexible to incorporate both kernel-based, (deep) neural network and convolutional based models within the same setting.
To update the parameters of the network, we propose a novel training procedure which jointly learns the features and shared representation.
Experiments demonstrate the potential of the framework through qualitative evaluation of generated samples.
In the past decade, interest in generative models has grown tremendously, finding applications in multiple fields such as, generated art, on-demand video, image denoising (Vincent et al., 2010) , exploration in reinforcement learning (Florensa et al., 2018) , collaborative filtering (Salakhutdinov et al., 2007) , inpainting (Yeh et al., 2017) and many more.
Some examples of graphical models based on a probabilistic framework with latent variables are Variational Auto-Encoders (Kingma & Welling, 2014) and Restricted Boltzmann Machines (RBMs) (Smolensky, 1986; Salakhutdinov & Hinton, 2009 ).
More recently proposed models are based on adversarial training such as Generative Adversarial Networks (GANs) (Goodfellow et al., 2014) and its many variants.
Furthermore, auto-regressive models such as Pixel Recurrent Neural Networks (PixelRNNs) (Van Den Oord et al., 2016) model the conditional distribution of every individual pixel given previous pixels.
All these approaches have their own advantages and disadvantages.
For example, RBMs perform both learning and Bayesian inference in graphical models with latent variables.
However, such probabilistic models must be properly normalized, which requires evaluating intractable integrals over the space of all possible variable configurations (Salakhutdinov & Hinton, 2009) .
Currently GANs are considered as the state-of-the-art for generative modeling tasks, producing high-quality images but are more difficult to train due to unstable training dynamics, unless more sophisticated variants are applied.
Many datasets are comprised of different representations of the data, or views.
Views can correspond to different modalities such as sounds, images, videos, sequences of previous frames, etc.
Although each view could individually be used for learning tasks, exploiting information from all views together could improve the learning quality (Pu et al., 2016; Liu & Tuzel, 2016; Chen & Denoyer, 2017) .
Also, it is among the goals of the latent variable modelling to model the description of data in terms of uncorrelated or independent components.
Some classical examples are Independent Component Analysis; Hidden Markov models (Rabiner & Juang, 1986) ; Probabilistic Principal Component Analysis (PCA) (Tipping & Bishop, 1999) ; Gaussian-Process Latent variable model (Lawrence, 2005) and factor analysis.
Hence, when learning a latent space in generative models, it becomes interesting to find a disentangled representation.
Disentangled variables are generally considered to contain interpretable information and reflect separate factors of variation in the data for e.g. lighting conditions, style, colors, etc.
The definition of disentanglement in the literature is not precise, however many believe that a representation with statistically independent variables is a good starting point (Schmidhuber, 1992; Ridgeway, 2016) .
Such representations extract information into a compact form which makes it possible to generate samples with specific characteristics (Chen et al., 2018; Bouchacourt et al., 2018; Tran et al., 2017; Chen et al., 2016) .
Additionally, these representations have been found to generalize better and be more robust against adversarial attacks (Alemi et al., 2017) .
In this work, we propose an alternative generative mechanism based on the framework of Restricted Kernel Machines (RKMs) (Suykens, 2017) , called Generative RKM (Gen-RKM).
RKMs yield a representation of kernel methods with visible and hidden units establishing links between Kernel PCA, Least-Squares Support Vector Machines (LS-SVM) (Suykens et al., 2002) and RBMs.
This framework has a similar energy form as RBMs, though there is a non-probabilistic training procedure where the eigenvalue decomposition plays the role of normalization.
Recently, Houthuys & Suykens (2018) used this framework to develop tensor-based multi-view classification models and Schreurs & Suykens (2018) showed how kernel PCA fits into this framework.
Contributions.
1) A novel multi-view generative model based on the RKM framework where multiple views of the data can be generated simultaneously.
2) Two methods are proposed for computing the pre-image of the feature vectors: with the feature map explicitly known or unknown.
We show that the mechanism is flexible to incorporate both kernel-based, (deep) convolutional neural network based models within the same setting.
3) When using explicit feature maps, we propose a training algorithm that jointly performs the feature-selection and learns the common-subspace representation in the same procedure.
4) Qualitative and quantitative experiments demonstrate that the model is capable of generating good quality images of natural objects.
Further experiments on multi-view datasets exhibit the potential of the model.
Thanks to the orthogonality of eigenvectors of the kernel matrix, the learned latent variables are uncorrelated.
This resembles a disentangled representation, which makes it possible to generate data with specific characteristics.
This paper is organized as follows.
In Section 2, we discuss the Gen-RKM training and generation mechanism when multiple data sources are available.
In Section 3, we explain how the model incorporates both kernel methods and neural networks through the use of implicit and explicit feature maps respectively.
When the feature maps are defined by neural networks, the Gen-RKM algorithm is explained in Section 4.
In Section 5, we show experimental results of our model applied on various public datasets.
Section 6 concludes the paper along with directions towards the future work.
Additional supplementary materials are given in the Appendix A.
The paper proposes a novel framework, called Gen-RKM, for generative models based on RKMs with extensions to multi-view generation and learning uncorrelated representations.
This allows for a mechanism where the feature map can be implicitly defined using kernel functions or explicitly by (deep) neural network based methods.
When using kernel functions, the training consists of only solving an eigenvalue problem.
In the case of a (convolutional) neural network based explicit feature map, we used (transposed) networks as the pre-image functions.
Consequently, a training procedure was proposed which involves joint feature-selection and subspace learning.
Thanks to training in mini-batches and capability of working with covariance matrices, the training is scalable to large datasets.
Experiments on benchmark datasets illustrate the merit of the proposed framework for generation quality as well as disentanglement.
Extensions of this work consists of adapting the model to more advanced multi-view datatsets involving speech, images and texts; further analysis on other feature maps, pre-image methods, loss-functions and uncorrelated feature learning.
Finally, this paper has demonstrated the applicability of the Gen-RKM framework, suggesting new research directions to be worth exploring.

assistant: text: Gen-RKM: a novel framework for generative models using Restricted Kernel Machines with multi-view generation and uncorrelated feature learning.