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Humans learn to solve tasks of increasing complexity by building on top ofpreviously acquired knowledge. Typically, there exists a natural progression inthe tasks that we learn - most do not require completely independent solutions,but can be broken down into simpler subtasks. We propose to represent a solverfor each task as a neural module that calls existing modules (solvers forsimpler tasks) in a functional program-like manner. Lower modules are a blackbox to the calling module, and communicate only via a query and an output.Thus, a module for a new task learns to query existing modules and composestheir outputs in order to produce its own output. Our model effectivelycombines previous skill-sets, does not suffer from forgetting, and is fullydifferentiable. We test our model in learning a set of visual reasoning tasks,and demonstrate improved performances in all tasks by learning progressively.By evaluating the reasoning process using human judges, we show that our modelis more interpretable than an attention-based baseline.

Visual Reasoning by Progressive Module Networks