Conditional Generative Adversarial Nets (1411.1784v1)

Abstract: Generative Adversarial Nets [8] were recently introduced as a novel way to train generative models. In this work we introduce the conditional version of generative adversarial nets, which can be constructed by simply feeding the data, y, we wish to condition on to both the generator and discriminator. We show that this model can generate MNIST digits conditioned on class labels. We also illustrate how this model could be used to learn a multi-modal model, and provide preliminary examples of an application to image tagging in which we demonstrate how this approach can generate descriptive tags which are not part of training labels.

• The paper introduces cGANs, where both generator and discriminator are conditioned on extra information to enable directed data synthesis.
• It demonstrates the model's versatility through experiments on the MNIST and MIR Flickr datasets for digit recognition and image tagging.
• Experimental results show comparable performance to standard GANs, highlighting potential for further optimization and broader applicability.

Conditional Generative Adversarial Nets

The paper "Conditional Generative Adversarial Nets" by Mehdi Mirza and Simon Osindero extends the model of Generative Adversarial Networks (GANs) by conditioning both the generator and discriminator on auxiliary information. This modification imparts control over the data generation process, thereby offering considerable flexibility in generating data based on specified conditions.

Introduction

Generative Adversarial Nets (GANs) were originally proposed to address the difficulties associated with approximating complex probabilistic computations inherent in traditional generative models. GANs consist of two adversarial neural networks: a generative model $G$ and a discriminative model $D$. The generator $G$ learns to capture the data distribution, while the discriminator $D$ estimates the probability that a sample originated from the real data rather than from $G$. The primary advantage of GANs is their ability to sidestep Markov chains and inference procedures typically required during learning.

However, the original GAN framework does not allow control over specific aspects of the generated data. This paper introduces Conditional GANs (cGANs) where the model is conditioned on additional information $y$, allowing for more directed data generation. The authors demonstrate this through empirical results on several datasets, showcasing the model's capacity to generate conditioned data.

Related Work

The paper reviews relevant work in multi-modal learning and image labeling. Supervised neural networks have seen substantial success in recent years, but challenges remain in scaling these models to large output categories and handling one-to-many mappings. Multi-modal models, such as Deep Boltzmann Machines (DBMs) and neural LLMs, have been proposed to address these issues by incorporating data from different modalities. These models facilitate semantic representations that enable robust predictive generalizations even to labels not encountered during training.

Conditional Adversarial Nets

Generative Adversarial Nets

GANs encompass two neural networks: a generator $G$ generating data samples and a discriminator $D$ distinguishing between real and generated samples. Both $G$ and $D$ are trained simultaneously to optimize a min-max objective function. The training involves adjusting parameters of $G$ to minimize $\log(1 - D(G(z)))$ and adjusting parameters of $D$ to maximize $\log D(x)$.

Conditional Adversarial Nets

The conditional model introduces conditioning variables $y$ to both the generator and discriminator. The generator $G(z|y)$ maps prior noise $z$ and conditioning variable $y$ to data space. Similarly, $D(x|y)$ discriminates between real and generated samples conditioned on $y$. The objective function is modified accordingly: $$\min_G \max_D V(D, G) = \mathbb{E}_{x \sim p_{\text{data}(x)}}[\log D(x|y)] + \mathbb{E}_{z \sim p_z(z)}[\log (1 - D(G(z|y)))].$$

Experimental Results

Unimodal

The authors trained a conditional adversarial network on the MNIST dataset, conditioning on digit class labels encoded as one-hot vectors. The network architecture included several hidden layers with ReLU activations and dropout for regularization. The results were evaluated using Gaussian Parzen window log-likelihood estimates, showing that the conditional adversarial nets perform comparably with other network-based approaches, though there's room for optimization to match the non-conditional models.

Multimodal

For the multimodal experiment, image features were extracted using a pre-trained convolutional model, and word representations were generated from user tags using a skip-gram model. The task was to generate tags for images from the MIR Flickr dataset. The generator received noise and image features, generating word vectors, while the discriminator evaluated the joint image-tag pair. The results demonstrated that the conditional adversarial net could produce plausible and relevant tags, although there's potential for improvement through more refined models and joint training schemes.

Implications and Future Work

The introduction of cGANs extends the applicability of GANs by enabling controlled data generation. This has theoretical implications for probabilistic modeling and practical applications in fields like image tagging, where conditioned generation is valuable. Future work may involve more sophisticated models, joint training schemes for LLMs, and further exploration of hyper-parameter space to enhance performance.

The conditional adversarial net approach opens new avenues in generative model research and applications in AI, providing a robust framework for conditioned data generation. Further exploration and optimization can potentially yield models that outperform non-conditional counterparts while offering greater control and flexibility.