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A deep neural network is a good task solver, but it is difficult to makesense of its operation. People have different ideas about how to form theinterpretation about its operation. We look at this problem from a newperspective where the interpretation of task solving is synthesized byquantifying how much and what previously unused information is exploited inaddition to the information used to solve previous tasks. First, after learningseveral tasks, the network acquires several information partitions related toeach task. We propose that the network, then, learns the minimal informationpartition that supplements previously learned information partitions to moreaccurately represent the input. This extra partition is associated withun-conceptualized information that has not been used in previous tasks. Wemanage to identify what un-conceptualized information is used and quantify theamount. To interpret how the network solves a new task, we quantify asmeta-information how much information from each partition is extracted. Weimplement this framework with the variational information bottleneck technique.We test the framework with the MNIST and the CLEVR dataset. The framework isshown to be able to compose information partitions and synthesizeexperience-dependent interpretation in the form of meta-information. Thissystem progressively improves the resolution of interpretation upon newexperience by converting a part of the un-conceptualized information partitionto a task-related partition. It can also provide a visual interpretation byimaging what is the part of previously un-conceptualized information that isneeded to solve a new task.

Progressive Interpretation Synthesis: Interpreting Task Solving by Quantifying Previously Used and Unused Information