Five key Advantages of using VAE over GAN are:
| Aspect |
VAEs Advantages |
GANs Advantages |
| Training Stability |
Less prone to training instability and mode collapse due to direct optimization of a well-defined loss function. |
Often more challenging to train due to adversarial loss, but recent advances improve stability with techniques like Wasserstein GANs. |
| Latent Space Structure |
Provides a well-defined, continuous, and interpretable latent space, making it easier to perform manipulations in the latent space. |
Latent space is less structured and harder to interpret, but modifications can still yield realistic outputs with additional tuning. |
| Likelihood Estimation |
Explicitly maximizes the likelihood of data, allowing better quantification of uncertainty and enabling probabilistic modeling. |
Does not provide an explicit likelihood estimation, making it less suitable for tasks that require probability estimation. |
| Diversity in Outputs |
A smooth latent distribution ensures diverse outputs, reducing the risk of mode collapse and improving data distribution coverage. |
Often produces sharper and more realistic images due to adversarial training, which directly optimizes visual fidelity. |
| Application Suitability |
Well-suited for applications needing latent space exploration (e.g., image reconstruction, interpolation, anomaly detection). |
It is ideal for high-quality image generation tasks where visual realism is prioritized, such as photorealistic image synthesis and style transfer. |
Related Post: Techniques for ensuring diverse sample generation in GANs and VAEs
