AI-woordenlijst
Het complete woordenboek van kunstmatige intelligentie
Encoder Discriminator
Critical component of the VAE-GAN where the discriminator a posteriori evaluates the decoder's reconstructions, forcing the encoder to produce informative latent representations for high-quality image generation.
Joint Loss Function
Loss function combining the VAE reconstruction loss, KL divergence, and GAN adversarial loss, simultaneously optimizing reconstruction accuracy and generation quality.
Latent Space Smoothness
Essential property of the VAE-GAN ensuring that continuous variations in the latent space produce semantically coherent variations in the generation space, facilitating interpolation and manipulation.
Reconstruction-Generation Trade-off
Delicate balance in VAE-GANs between VAE reconstruction fidelity and GAN perceptual quality, requiring precise adjustment of loss weights to optimize overall performance.
Perceptual Loss Integration
Incorporation of pre-trained perceptual metrics into the VAE-GAN loss function to evaluate semantic similarity rather than pixel-by-pixel, thereby improving the visual quality of generations.
Variational Inference in GAN
Application of variational inference principles to the GAN framework, enabling learning of approximate posterior distributions and better uncertainty modeling in generation.
Encoder-Decoder Consistency
Constraint ensuring that encoding a generated image follows the same distribution as encoding real images, maintaining cyclic consistency between encoder and decoder in the VAE-GAN.
Conditional VAE-GAN
Extension of the VAE-GAN integrating conditional information (classes, attributes) into encoding and generation, enabling precise control over the characteristics of generated samples.
Hierarchical VAE-GAN
Multi-scale architecture combining multiple levels of VAE-GAN to capture hierarchical structures in data, from global features to fine details.
Progressive Growing VAE-GAN
Training strategy where the resolution of generations increases progressively, stabilizing learning and improving the final quality of high-resolution generated images.