Generative AI
Prizm AI
DCGAN-based Image Synthesis System
Designed high-fidelity image generation system with VGG16 discriminator, reducing mode collapse by 30%. Implemented DCGAN architecture with LeakyReLU and Dropout layers, enabling users to dynamically synthesize high-quality images in under 2 seconds per iteration using Adam optimizer.
4 months
Project Duration
30% mode collapse reduction
Key Achievement
5+
Technologies Used
The Challenge
Prizm AI required a high-fidelity image generation system that could produce diverse, realistic images while minimizing mode collapse issues common in GANs.
The Solution
Designed and implemented a DCGAN architecture with VGG16 discriminator, incorporating advanced techniques like LeakyReLU activation, strategic dropout, and Adam optimization for stable training.
Results & Impact
- Reduced mode collapse by 30% compared to baseline models
- Achieved sub-2 second generation time per iteration
- Generated high-quality, diverse image outputs
- Improved training stability and convergence
Project Metrics
30% mode collapse reduction
<2 second per iteration
Technologies Used
DCGANVGG16LeakyReLUDropoutAdam Optimizer
Project Details
Company
Prizm AI
Duration
4 months
Team Size
2 engineers + 1 researcher
Category
Generative AI