ClusterGAN : Latent Space Clustering in Generative Adversarial Networks (1809.03627v2)

Abstract: Generative Adversarial networks (GANs) have obtained remarkable success in many unsupervised learning tasks and unarguably, clustering is an important unsupervised learning problem. While one can potentially exploit the latent-space back-projection in GANs to cluster, we demonstrate that the cluster structure is not retained in the GAN latent space. In this paper, we propose ClusterGAN as a new mechanism for clustering using GANs. By sampling latent variables from a mixture of one-hot encoded variables and continuous latent variables, coupled with an inverse network (which projects the data to the latent space) trained jointly with a clustering specific loss, we are able to achieve clustering in the latent space. Our results show a remarkable phenomenon that GANs can preserve latent space interpolation across categories, even though the discriminator is never exposed to such vectors. We compare our results with various clustering baselines and demonstrate superior performance on both synthetic and real datasets.

Insights into "ClusterGAN: Latent Space Clustering in Generative Adversarial Networks"

The paper "ClusterGAN: Latent Space Clustering in Generative Adversarial Networks" presents a novel methodology to achieve clustering within the latent spaces of Generative Adversarial Networks (GANs). This research addresses a key challenge in unsupervised learning by demonstrating how GANs, which traditionally did not retain cluster structures in their latent space, can be augmented to perform effective clustering. The authors introduce an innovative variant, ClusterGAN, that enhances the GAN architecture to facilitate latent space clustering, providing meaningful improvements in performance over conventional methods.

Core Innovations

The primary innovation lies in the hybridization of discrete and continuous latent variables. ClusterGAN samples latent variables from a sophisticated mixture of one-hot encoded categorical variables and Gaussian distributed continuous variables. This configuration is critical as it introduces non-smooth geometry that is conducive to clustering, a feature that traditional GANs lack. ClusterGAN also incorporates an inverse network trained concurrently with the generator and discriminator, thus ensuring that the latent space reflects the clustering-geometries of the data.

1. Discrete-Continuous Latent Space: By interspersing discrete one-hot vectors with continuous vectors, ClusterGAN ensures a non-smooth latent space conducive to clustering.
2. Back-Propagation Algorithm: The proposed backpropagation method accounts for the optimizations necessary for handling mixtures of discrete and continuous distributions, leading to more effective clustering dynamics.
3. Joint Training Protocol: ClusterGAN jointly trains the generator, discriminator, and an inverse network with a clustering-specific loss, facilitating a more accurate mapping from data to latent space clusters.

Empirical Results and Performance

ClusterGAN's efficacy is highlighted through empirical comparison with established clustering methods and other GAN-based techniques such as InfoGAN. The architecture achieves significant improvements across synthetic datasets, MNIST, Fashion-MNIST, cell-type gene expression data, and more. Notably, the method showcases high accuracy (ACC) and normalized mutual information (NMI) metrics, indicating enhanced clustering performance. The paper also discusses the scalability of ClusterGAN to datasets with numerous clusters, demonstrating its robustness and adaptability.

Beyond clustering, ClusterGAN retains the traditional GAN capability of producing high-quality sample interpolations. This is particularly notable as direct interpolations between clusters—formed by varying mixtures of discrete latent vectors—are naturally achieved without compromising semantic coherence, even though such interpolations were not part of the training data.

Theoretical and Practical Implications

The development of ClusterGAN holds substantial implications for both theoretical advancements and practical applications:

• Theory of GANs: By bridging the gap between discrete and continuous latent space representations, ClusterGAN expands the theoretical understanding of GAN architectures to include unsupervised clustering tasks.
• Practical Applications: ClusterGAN can potentially impact domains where generating labeled data is expensive, such as single-cell transcriptomics or large, unlabeled image datasets. The ability to cluster high-dimensional data efficiently opens new pathways for data exploration and analysis in these fields.

Future Directions

The paper invites exploration into several future research areas:

• Data-Driven Priors: Investigating adaptive priors for the latent space could refine the clustering capabilities of GANs further.
• Sparse Data Generative Models: Extending the approach to other data types, particularly those represented in sparse generative structures, could enhance applications in fields such as compressed sensing.

ClusterGAN sets a foundational precedent for latent space clustering using generative models, furnishing the scientific community with a tool that bridges generative capabilities and effective data segmentation, and paving the way for future innovations in unsupervised learning methodologies.