Foundation models [6] such as CLIP [47] have been extensively utilized on diverse domains via pretrain-finetune approaches.

Learning general-purpose representations from perceptual inputs is a hallmark of human intelligence. For example, people can write out numbers or characters, or even draw doodles, by characterizing these tasks as different instantiations of the same generic underlying process---compositional arrangements of different forms of pen strokes. Crucially, learning to do one task, say writing, implies reasonable competence at another, say drawing, on account of this shared process. We present Drawing out of Distribution (DooD), a neuro-symbolic generative model of stroke-based drawing that can learn such general-purpose representations. In contrast to prior work, DooD operates directly on images, requires no supervision or expensive test-time inference, and performs unsupervised amortized inference with a symbolic stroke model that better enables both interpretability and generalization. We evaluate DooD on its ability to generalize across both data and tasks. We first perform zero-shot transfer from one dataset (e.g.

All dataset images are scaled to 50x50 in grayscale, with dataset-specific configuration list below. For one-shot classification ( 3.2), we use the original task-split, as found on It has 20 episodes, each a 20-way, 1-shot, within-alphabet classification task. Bézier curves are parametric curves commonly used in computer graphics to define smooth, continuous curves. As an effect of this rasterizing procedure, the pixel intensity can be arbitrarily large. B.3 Neural Network Configurations DooD and AIR in our experiments share the overall neural components.

Learning general-purpose representations from perceptual inputs is a hallmark of human intelligence.

Learning general-purpose representations from perceptual inputs is a hallmark of human intelligence. For example, people can write out numbers or characters, or even draw doodles, by characterizing these tasks as different instantiations of the same generic underlying process---compositional arrangements of different forms of pen strokes. Crucially, learning to do one task, say writing, implies reasonable competence at another, say drawing, on account of this shared process. We present Drawing out of Distribution (DooD), a neuro-symbolic generative model of stroke-based drawing that can learn such general-purpose representations. In contrast to prior work, DooD operates directly on images, requires no supervision or expensive test-time inference, and performs unsupervised amortized inference with a symbolic stroke model that better enables both interpretability and generalization.