Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizer (1807.07543v2)

Abstract: Autoencoders provide a powerful framework for learning compressed representations by encoding all of the information needed to reconstruct a data point in a latent code. In some cases, autoencoders can "interpolate": By decoding the convex combination of the latent codes for two datapoints, the autoencoder can produce an output which semantically mixes characteristics from the datapoints. In this paper, we propose a regularization procedure which encourages interpolated outputs to appear more realistic by fooling a critic network which has been trained to recover the mixing coefficient from interpolated data. We then develop a simple benchmark task where we can quantitatively measure the extent to which various autoencoders can interpolate and show that our regularizer dramatically improves interpolation in this setting. We also demonstrate empirically that our regularizer produces latent codes which are more effective on downstream tasks, suggesting a possible link between interpolation abilities and learning useful representations.

• The paper introduces ACAI, an adversarial regularizer that sharpens interpolation quality in autoencoders by training a critic network to recover mixing coefficients.
• The methodology features a synthetic benchmark using 2D line drawings to quantitatively assess interpolation against models like VAE, DAE, and AAE.
• Quantitative results show that improved interpolation correlates with enhanced downstream performance, including classification and clustering on MNIST, SVHN, and CIFAR-10.

Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizer

The paper undertakes a systematic investigation into the interpolation capabilities of autoencoders and introduces an innovative technique aimed at enhancing these capabilities through adversarial regularization, culminating in the creation of an Adversarially Constrained Autoencoder Interpolation (ACAI). The research explores both theoretical and practical aspects of autoencoders, a prevalent unsupervised learning framework used for mapping data to a latent code and subsequently reconstructing it with minimal information loss.

Key Contributions

The paper offers several notable contributions:

1. Introduction of Adversarial Regularization: The principal contribution is the development of an adversarial regularization strategy designed to improve the interpolation quality in autoencoders. By exploiting a critic network trained to recover mixing coefficients from interpolated data, the regularizer encourages the autoencoder to produce realistic interpolated data that appear indistinguishable from genuine data samples.
2. Benchmark for Measuring Interpolation Quality: The authors propose a synthetic benchmark task, involving 2D line drawings, where interpolation abilities can be quantitatively assessed. This benchmark establishes a concrete and straightforward mechanism for measuring the extent of interpolation across various autoencoder architectures.
3. Quantitative Evaluation: Extensive quantitative evaluation highlights that the proposed regularizer significantly enhances interpolation quality compared to existing autoencoder variants. Synthetic tasks confirmed that the ACAI outperformed established models, including Variational Autoencoders (VAE), Denoising Autoencoders, and Adversarial Autoencoders (AAE), in producing realistic and smoothly transitioning interpolations.
4. Impact on Representation Learning: The paper extends its analysis to the effects of the adversarial regularizer on representation learning. Improved interpolation capabilities are shown to correlate with enhanced performance on downstream tasks, such as classification and clustering, using the learned latent representations on datasets like MNIST, SVHN, and CIFAR-10.

Implications and Future Directions

The implications of this research are significant, providing a pathway for enhancing the latent space structure which can be invaluable for generative tasks, creative applications, and representation learning. The adversarial approach to regularizing interpolation not only improves the autoencoder's generative capabilities, but also potentially offers insights into the connection between realistic interpolation and the disentanglement of latent variables.

Looking forward, the proposed framework may inspire further exploration into the generalization and scalability of adversarial techniques across various unsupervised learning models. Additionally, there exists a possibility for extending the framework to non-image datasets, which was not addressed in this paper. Future work could also investigate the application of adversarial constraints across other forms of latent-variable models to synergistically enhance their learning and generative performance.

This paper serves as an important contribution to the understanding and advancement of autoencoders by resolving a critical ambiguity in their interpolation behavior, thus proposing an approach that effectively bridges the gap between theoretical promise and practical implementation.