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Score-based models have recently been introduced as a richer framework tomodel distributions in high dimensions and are generally more suitable forgenerative tasks. In score-based models, a generative task is formulated usinga parametric model (such as a neural network) to directly learn the gradient ofsuch high dimensional distributions, instead of the density functionsthemselves, as is done traditionally. From the mathematical point of view, suchgradient information can be utilized in reverse by stochastic sampling togenerate diverse samples. However, from a computational perspective, existingscore-based models can be efficiently trained only if the forward or thecorruption process can be computed in closed form. By using the relationshipbetween the process and layers in a feed-forward network, we derive abackpropagation-based procedure which we call Intermediate GeneratorOptimization to utilize intermediate iterates of the process with negligiblecomputational overhead. The main advantage of IGO is that it can beincorporated into any standard autoencoder pipeline for the generative task. Weanalyze the sample complexity properties of IGO to solve downstream tasks likeGenerative PCA. We show applications of the IGO on two dense predictive tasksviz., image extrapolation, and point cloud denoising. Our experiments indicatethat obtaining an ensemble of generators for various time points is possibleusing first-order methods.

Using Intermediate Forward Iterates for Intermediate Generator Optimization