On the Effects of Batch and Weight Normalization in Generative Adversarial Networks (1704.03971v4)

Abstract: Generative adversarial networks (GANs) are highly effective unsupervised learning frameworks that can generate very sharp data, even for data such as images with complex, highly multimodal distributions. However GANs are known to be very hard to train, suffering from problems such as mode collapse and disturbing visual artifacts. Batch normalization (BN) techniques have been introduced to address the training. Though BN accelerates the training in the beginning, our experiments show that the use of BN can be unstable and negatively impact the quality of the trained model. The evaluation of BN and numerous other recent schemes for improving GAN training is hindered by the lack of an effective objective quality measure for GAN models. To address these issues, we first introduce a weight normalization (WN) approach for GAN training that significantly improves the stability, efficiency and the quality of the generated samples. To allow a methodical evaluation, we introduce squared Euclidean reconstruction error on a test set as a new objective measure, to assess training performance in terms of speed, stability, and quality of generated samples. Our experiments with a standard DCGAN architecture on commonly used datasets (CelebA, LSUN bedroom, and CIFAR-10) indicate that training using WN is generally superior to BN for GANs, achieving 10% lower mean squared loss for reconstruction and significantly better qualitative results than BN. We further demonstrate the stability of WN on a 21-layer ResNet trained with the CelebA data set. The code for this paper is available at https://github.com/stormraiser/gan-weightnorm-resnet

• The paper reveals that Weight Normalization reduces reconstruction loss by about 10% compared to Batch Normalization.
• The study employs DCGAN and ResNet models across datasets like CelebA and CIFAR-10 to assess training dynamics.
• The evaluation demonstrates that Weight Normalization leads to more stable GAN training and superior overall performance.

Effects of Batch and Weight Normalization in GANs

The paper "On the Effects of Batch and Weight Normalization in Generative Adversarial Networks" examines the role of normalization techniques in the training stability and quality of GANs. The paper's central focus is on comparing Batch Normalization (BN) and a proposed Weight Normalization (WN) in the context of GANs. The researchers embarked on an empirical investigation to determine the efficacy, stability, and performance of these normalization techniques when incorporated into GAN architectures.

Key Insights and Methodologies

Generative Adversarial Networks are influential frameworks in unsupervised learning, notable for their ability to craft high-fidelity data, such as complex and multimodal image distributions. However, they exhibit inherent difficulties in training, including mode collapse and the presence of inadequate visual artifacts—motives that warrant the investigation into normalization techniques. Batch Normalization, despite its widespread use and initial acceleration in training, is critiqued in the paper for introducing instability and restrictive impacts on GANs' capacity to generalize well.

To address these issues, a novel approach called Weight Normalization is introduced. Originating partially from prior works on diverse normalization methods, the paper builds a modified framework that seeks to ameliorate previous weaknesses noticed with BN and traditional WN implementations. A rigorous evaluation criterion—squared Euclidean reconstruction error on test datasets—guides the methodical comparison of the training processes.

The practical evaluation utilized standard DCGAN architectures across notable datasets like CelebA, LSUN bedrooms, and CIFAR-10. An additional test-case examined a 21-layer ResNet model trained with CelebA, seeking deeper insights into complex model structures.

Experimental Findings

The experimental results favor Weight Normalization over Batch Normalization for GANs under the tested conditions. Notably, WN demonstrated a:

• 10% Reduction in Mean Squared Reconstruction Loss: When evaluated on test datasets, WN realized significantly lower error rates, which translates to enhanced fidelity of generated samples, even for convoluted distributions.
• Stability and Performance Gains: The use of WN resulted in more stable models throughout extensive training iterations, supporting a complex architecture like ResNet without reverting to collapse or overfitting.
• Comparable or Accelerated Training Against BN: While early-stage training shows BN assisting in speed, WN manages to sustain the composure and quality of learning at later stages, a crucial aspect for the holistic development of generative models.

Theoretical and Practical Implications

The research underscores critical theoretical bearings in GAN configurations, particularly the justifiability of normalization techniques. Through extensive experimental design, it is revealed that the nuanced application of normalization can profoundly influence both the generation quality and training dynamics.

This work sets precedence for future explorations into combining robust normalization methodologies with other GAN optimizations. For practitioners deploying GANs in practical settings, substituting BN with WN could potentially result in more reliable outputs and foster higher confidence in the deployment of complex generative models across varied domains.

Future Directions

Further exploration of WN's adaptability with other architectural enhancements and loss modification techniques could yield substantial advancements. The suggested complementarity with existing GAN techniques opens pathways for hybrid approaches that strive to mitigate stability issues and enhance generation quality concurrently.

In sum, this paper provides a pivotal examination into the normalization practices in GAN training. By proposing and validating a refined normalization technique—Weight Normalization—it lays the groundwork for more efficient and stable GAN development, holding significant promise for the trajectory of generative modeling research.