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Title: PAIRWISE AUGMENTED GANS WITH ADVERSARIAL RECONSTRUCTION LOSS

Paper content:
We propose a novel autoencoding model called Pairwise Augmented GANs.
We train a generator and an encoder jointly and in an adversarial manner.
The generator network learns to sample realistic objects.
In turn, the encoder network at the same time is trained to map the true data distribution to the prior in  latent space.
To ensure good reconstructions, we introduce an augmented adversarial reconstruction loss.
Here we train a discriminator to distinguish two types of pairs: an object with its augmentation and the one with its reconstruction.
We show that such adversarial loss compares objects based on the content rather than on the exact match.
We experimentally demonstrate that our model generates samples and reconstructions of quality competitive with state-of-the-art on datasets MNIST, CIFAR10, CelebA and achieves good quantitative results on CIFAR10.
Deep generative models are a powerful tool to sample complex high dimensional objects from a low dimensional manifold.
The dominant approaches for learning such generative models are variational autoencoders (VAEs) and generative adversarial networks (GANs) BID12 .
VAEs allow not only to generate samples from the data distribution, but also to encode the objects into the latent space.
However, VAE-like models require a careful likelihood choice.
Misspecifying one may lead to undesirable effects in samples and reconstructions (e.g., blurry images).
On the contrary, GANs do not rely on an explicit likelihood and utilize more complex loss function provided by a discriminator.
As a result, they produce higher quality images.
However, the original formulation of GANs BID12 lacks an important encoding property that allows many practical applications.
For example, it is used in semi-supervised learning , in a manipulation of object properties using low dimensional manifold BID7 and in an optimization utilizing the known structure of embeddings BID11 .VAE-GAN
hybrids are of great interest due to their potential ability to learn latent representations like VAEs, while generating high-quality objects like GANs. In such
generative models with a bidirectional mapping between the data space and the latent space one of the desired properties is to have good reconstructions (x ≈ G(E(x))). In many
hybrid approaches BID30 BID34 BID36 BID3 BID33 as well as in VAE-like methods it is achieved by minimizing L 1 or L 2 pixel-wise norm between x and G(E(x)). However
, the main drawback of using these standard reconstruction losses is that they enforce the generative model to recover too many unnecessary details of the source object x. For example
, to reconstruct a bird picture we do not need an exact position of the bird on an image, but the pixel-wise loss penalizes a lot for shifted reconstructions. Recently,
improved ALI model BID8 by introducing a reconstruction loss in the form of a discriminator which classifies pairs (x, x) and (x, G(E(x))). However,
in such approach, the discriminator tends to detect the fake pair (x, G(E(x))) just by checking the identity of x and G(E(x)) which leads to vanishing gradients.In this paper, we propose a novel autoencoding model which matches the distributions in the data space and in the latent space independently as in BID36 . To ensure
good reconstructions, we introduce an augmented adversarial reconstruction loss as a discriminator which classifies pairs (x, a(x)) and (x, G(E(x))) where a(·) is a stochastic augmentation function. This enforces
the DISPLAYFORM0 discriminator to take into account content invariant to the augmentation, thus making training more robust. We call this
approach Pairwise Augmented Generative Adversarial Networks (PAGANs).Measuring a reconstruction
quality of autoencoding models is challenging. A standard reconstruction
metric RMSE does not perform the content-based comparison. To deal with this problem
we propose a novel metric Reconstruction Inception Dissimilarity (RID) which is robust to content-preserving transformations (e.g., small shifts of an image). We show qualitative results
on common datasets such as MNIST BID19 , CIFAR10 BID17 and CelebA BID21 . PAGANs outperform existing
VAE-GAN hybrids in Inception Score BID31 and Fréchet Inception Distance BID14 except for the recently announced method PD-WGAN BID10 on CIFAR10 dataset.
In this paper, we proposed a novel framework with an augmented adversarial reconstruction loss.
We introduced RID to estimate reconstructions quality for images.
It was empirically shown that this metric could perform content-based comparison of reconstructed images.
Using RID, we proved the value of augmentation in our experiments.
We showed that the augmented adversarial loss in this framework plays a key role in getting not only good reconstructions but good generated images.Some open questions are still left for future work.
More complex architectures may be used to achieve better IS and RID.
The random shift augmentation may not the only possible choice, and other smart choices are also possible.

assistant: text: We propose a novel autoencoding model with augmented adversarial reconstruction loss. We intoduce new metric for content-based assessment of reconstructions.