Generative Deep Learning

Latent Space Dimensionalitydesign lever

The number of dimensions in the learned latent representation space of a generative model. Higher dimensionality allows more expressive encoding of complex data such as faces, but risks sparse coverage and sampling difficulties if not regularized.

KL Divergence Loss Weightingdesign lever

The scalar coefficient (r_loss_factor) that balances the reconstruction loss against the Kullback-Leibler divergence penalty in the VAE objective, controlling how strongly the latent distribution is pushed toward a standard normal and therefore how continuous and well-covered the latent space is.

Adversarial Loss Function Choicedesign lever

The choice of loss function used to train the GAN discriminator or critic and generator, ranging from binary cross-entropy (vanilla GAN) to Wasserstein loss (WGAN) to Wasserstein loss with gradient penalty (WGAN-GP). Each choice has distinct theoretical properties governing gradient informativeness and training stability.

Attention Mechanism Inclusiondesign lever

Whether the model architecture incorporates an attention mechanism that computes a weighted sum over all previous hidden states rather than relying solely on the final hidden state or a fixed-size context vector. Enables selective focus on relevant prior timesteps in sequential generation tasks.

Normalization Strategydesign lever

The type of normalization layer applied within the network, including batch normalization, instance normalization, layer normalization, or adaptive instance normalization (AdaIN). Each strategy makes different assumptions about how feature statistics should be computed and applied, affecting training stability, style control, and output quality.

Progressive Training Scheduledesign lever

A training protocol in which the generator and discriminator begin training at low spatial resolution and progressively add higher-resolution layers during training, allowing earlier layers to stabilize before finer details are introduced. Used in ProGAN to improve stability and final image quality.

Latent Space Continuitypsychological state

The degree to which the learned latent space is locally and globally continuous, meaning that nearby points in latent space decode to perceptually similar outputs and no large gaps exist between point clusters. Continuity enables reliable sampling and smooth interpolation between generated outputs.

Training Stabilitybehavioral pattern

The degree to which the loss functions of the generator and discriminator or critic converge smoothly over training batches without oscillating wildly, diverging, or collapsing to degenerate solutions such as mode collapse or exploding gradients. Stable training is a prerequisite for achieving high output quality.

Latent Representation Qualitypsychological state

The quality and informativeness of the learned latent representations, reflecting how well the encoder has captured the high-level features of the training data (e.g., hair color, facial expression, musical key) in a compact, disentangled form that supports generation and manipulation tasks.

Long-Range Dependency Modeling Capacitybehavioral pattern

The ability of the model to capture and exploit statistical dependencies between elements of a sequence that are separated by many timesteps, such as the recapitulation of a musical motif or pronoun reference in text. Critical for generating coherent long-form sequences with consistent structure.

Mode Collapsebehavioral pattern

A failure mode in GAN training where the generator maps many or all points in the latent space to a small set of output modes, resulting in low diversity of generated samples. It occurs when the generator finds a narrow set of outputs that consistently fool the current discriminator and loses sensitivity to its latent input.

Reconstruction Fidelitybehavioral pattern

The degree to which a generative model's decoder can reconstruct the original input image or sequence from its latent encoding, measured by pixel-level or token-level similarity between the original and reconstructed output. High fidelity indicates the encoder-decoder pair has captured sufficient information about the data.

Generated Output Qualityoutcome metric

The perceptual realism and aesthetic quality of samples produced by the generative model when sampling from the latent distribution, including sharpness, coherence, absence of artifacts, and fidelity to the style or content of the training domain. The primary output-level success criterion for generative models.

Generated Output Diversityoutcome metric

The variety and coverage of distinct modes in the generated sample distribution, reflecting the model's ability to produce a wide range of plausible outputs rather than concentrating on a narrow subset of the training distribution. Complementary to output quality; together they define generative model success.

Downstream Task Performanceoutcome metric

The performance of an agent or system that uses the generative model as a component—for example, the cumulative reward score achieved by a reinforcement learning agent that uses a VAE and MDN-RNN as its world model, or the translation BLEU score of a Transformer. Represents the ultimate real-world utility of the generative model.

Lipschitz Constraint Enforcementcontextual condition

The degree to which the critic function in a Wasserstein GAN satisfies the 1-Lipschitz continuity requirement, meaning the absolute gradient norm of the critic's predictions with respect to its inputs is bounded at 1 everywhere. Proper enforcement is necessary for the Wasserstein loss to provide valid, informative gradients to the generator.

World Model Accuracybehavioral pattern

The accuracy with which the MDN-RNN component of the world model predicts the distribution of the next latent state and reward, given the current latent state and action. Higher accuracy means the dream environment better approximates the real environment, enabling in-dream training to generalize effectively to the real world.