Deep Convolutional GAN
— GANs Series Part 2
The intuition behind Deep Convolutional Generative Adversarial Networks (DCGAN) lies in leveraging the power of convolutional neural networks for image generation tasks within the framework of Generative Adversarial Networks (GANs). DCGANs are specifically designed to generate high-quality images that closely resemble real data samples.
Generator Network:
The generator network in DCGAN takes random noise as input and generates synthetic data, usually images, with the goal of generating realistic data samples that resemble the training data.
1. The generator typically consists of multiple layers of transposed convolutions, batch normalization, and activation functions like ReLU, with the final layer, using a tanh activation function to generate data in the range of [-1, 1].
2. The transposed convolutions help to upsample the noise input and gradually generate higher-resolution images.
3. The generator aims to fool the discriminator by producing high-quality and realistic images that are indistinguishable from real data.
Discriminator Network:
The discriminator network in DCGAN is responsible for discriminating between real data samples from the training dataset and fake data samples generated by the generator.
1. The discriminator typically consists of multiple convolutional layers, batch normalization, and activation functions like Leaky ReLU.
2. The discriminator outputs a probability score indicating the likelihood that the input data is real.
3. The discriminator is trained to correctly classify real data as real (outputting a high probability) and fake data as fake (outputting a low probability).
4. The discriminator aims to differentiate between real and generated data samples accurately.
Description of training losses:
Generator Loss:
The generator aims to generate realistic images that can potentially fool the discriminator into classifying them as real. The loss function used to train the generator in a DCGAN is based on the binary cross-entropy loss. The generator strives to minimize this loss, which effectively encourages it to produce images that the discriminator is more likely to classify as real.
The generator loss can be formulated as:
Generator Loss = -log(D(G(z)))
Where:
- (G(z)) is the generated image produced by the generator from a random noise vector (z).
- (D(G(z))) is the output of the discriminator when fed with the generated image.
Discriminator Loss:
The discriminator, on the other hand, aims to accurately distinguish between real images from the dataset and the generated images from the generator. The discriminator’s loss function is based on binary cross-entropy. The discriminator seeks to maximize this loss by correctly classifying real images as real and generated images as fake.
The discriminator loss can be formulated as:
Discriminator Loss = -log(D(x)) -log(1 -D(G(z)))
Where:
- (D(x)) is the output of the discriminator when fed with a real image ( x ) from the dataset.
- (D(G(z))) is the output of the discriminator when fed with a generated image from the generator.
Challenges and problems faced by BCE loss in GANs:
1) Mode collapse: BCE loss can lead to the problem of mode collapse, where the generator produces a limited variety of outputs and fails to capture the full distribution of the training data.
For example, mode collapse happens when the generator gets stuck in one mode. (It learns to fool the discriminator, by learning the examples from a single class and leaving the rest of the classes).
2) Vanishing gradient: BCE loss can suffer from the vanishing gradient problem, where the gradients become extremely small and slow down the training process.
3) Training instability: GANs with BCE loss can face training instability, where the discriminator becomes too strong or the generator fails to generate realistic samples.
Conclusion:
Overall, DCGANs leverage the power of deep convolutional neural networks to generate realistic images with high fidelity and diversity, while ensuring that the discriminator is effective in distinguishing between real and fake data. By using convolutional layers, spatial information in images is preserved, allowing for more realistic image generation. DCGANs have been successful in various image generation tasks, such as generating faces, bedrooms, and other complex images.