Approach to construction of hybrid AI models optimized by Deep Neuroevolution, with ML, AI and DL
The CNN-VAE is an advanced autoencoder architecture that combines Convolutional Neural Networks (CNNs) with Variational Autoencoders (VAEs). It is particularly effective for processing and generating high-dimensional data such as images.
- Purpose: The CNN component is used in the encoder to extract features from input images.
- Advantage: Convolutional layers excel at capturing spatial hierarchies and patterns in images, making them well-suited for visual data.
- Purpose: The VAE component focuses on learning a probabilistic mapping from the input space to a latent space.
- Structure:
- Encoder: Maps the input data to a distribution in the latent space, typically producing parameters for a Gaussian distribution (mean and variance).
- Decoder: Samples from this latent distribution and reconstructs the original input data from these samples.
- Purpose: Captures the underlying structure of the data.
- Benefit: Encourages learned latent representations to be continuous and normally distributed, which aids in generating new, similar data samples.
The CNN-VAE employs a combined loss function consisting of:
- Reconstruction Loss: Measures how well the decoder reconstructs the original input from the latent space. Common metrics include binary cross-entropy or mean squared error.
- KL Divergence: Ensures that the distribution learned by the encoder is close to a standard normal distribution. This term regularizes the model and helps prevent overfitting.
The CNN-VAE architecture leverages CNNs to effectively process and encode image data and VAEs to learn a meaningful latent space and generate new images. This combination enables powerful generative models capable of creating new, high-quality images similar to the training data.