GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution (1611.04051v1)

Abstract: Generative Adversarial Networks (GAN) have limitations when the goal is to generate sequences of discrete elements. The reason for this is that samples from a distribution on discrete objects such as the multinomial are not differentiable with respect to the distribution parameters. This problem can be avoided by using the Gumbel-softmax distribution, which is a continuous approximation to a multinomial distribution parameterized in terms of the softmax function. In this work, we evaluate the performance of GANs based on recurrent neural networks with Gumbel-softmax output distributions in the task of generating sequences of discrete elements.

• The paper introduces a Gumbel-softmax approach that makes GANs differentiable for generating discrete sequences.
• It integrates the method with LSTM-based architectures to produce arithmetic sequence examples effectively.
• Experimental results show stable convergence and potential for broader applications in language processing and synthetic biology.

Generative Adversarial Networks for Discrete Sequences Using the Gumbel-Softmax Distribution

This paper investigates an innovative approach to training Generative Adversarial Networks (GANs) for generating sequences of discrete elements. The primary challenge addressed by the authors is the non-differentiability of discrete data, which emerges when GANs are used in tasks requiring the generation of sequences such as text or molecular structures represented by SMILES strings.

Limitations of Traditional GANs on Discrete Data

Traditional GANs rely on the backpropagation of gradients from the discriminator to fine-tune the generator's parameters. This technique works seamlessly for continuous data, where derivatives can be directly computed. However, when applied to discrete data, the backpropagated gradients result in zero, posing a significant obstacle. The non-differentiability of sequences derived from a multinomial distribution impedes the application of gradient descent methods crucial for GAN training.

The Gumbel-Softmax Distribution

The authors propose using the Gumbel-softmax distribution, a differentiable approximation to the multinomial distribution. This technique enables the generation of discrete samples while maintaining a differentiable form, allowing gradient-based training of the high-dimensional and complex models within a GAN framework. The Gumbel-softmax distribution applies a continuous relaxation of discrete variables, parameterized by an inverse temperature parameter, $\tau$, which controls the approximation's smoothness. As $\tau$ approaches zero, the distribution becomes equivalent to the one-hot encoded outputs typically produced by the multinomial distribution.

Implementation Using Recurrent Neural Networks

To highlight the efficacy of their approach, the authors integrate the Gumbel-softmax trick into a recurrent neural network (RNN) architecture, specifically using Long Short-Term Memory (LSTM) units. This architecture is tasked with generating simple arithmetic sequences, serving as a manageable yet instructive example of discrete data generation. The GAN framework is adapted by feeding noise samples into the generator LSTM, which then attempts to create sequences indistinguishable from real data, while the discriminator LSTM is trained to differentiate between generated and authentic sequences.

Experimental Insights and Results

Experimental results demonstrated the model's ability to generate coherent sequences from a context-free grammar with promising accuracy. The generator and discriminator losses authenticated the adversarial training's convergence to an equilibrium where the generator's outputs resistantly mimicked real sequences, despite the discriminator's attempts to classify them correctly. Additionally, the model's flexibility in handling variable sequence lengths and its adaptability across variations in temperature schedules underline its potential robustness.

Implications and Future Directions

The approach suggests a viable solution for applying GANs to discrete domains previously inaccessible due to differentiability issues, broadening the potential applications in natural language processing and synthetic biology, among others. The work also paves the way for further exploration into advanced GAN training techniques, such as integrating variational divergence minimization or density ratio estimation to refine performance and generalizability further.

In conclusion, the paper provides a meaningful contribution to the field by proposing a method to circumvent traditional GAN limitations when working with discrete sequences, enhancing GAN applicability in discrete sequence data generation tasks. Considering the empirical results and theoretical framework, the Gumbel-softmax-enhanced GAN architecture appears to be a promising direction for future research in generative models.