Existing content-based image retrieval models work well for natural photos, but not for images of architectural floor plans.
Previous work on floor plan retrieval has focused on graph-based methods, rather than image-based floor plans.
Training a CNN-based representation learning framework on segmented floor plan images with standard image augmentations does not result in semantically meaningful retrievals.
This work shows that a CNN-based representation learning model can learn features for retrieving floor plans that have similar graphs given the right training signal. Two methods were investigated here: GeomPerturb, a data augmentation that perturbs the underlying geometry of a floor plan, and a weakly supervised method with labels based on the graph edit distance between a pair of floor plans. The results show that while GeomPerturb learns representations that are correlated with the floor plan graph, training with GED labels leads to better retrievals both in terms of the floor plan graph and with respect to room shapes.

Previous research has in reinforcement learning for traffic control has used various state abstractions. Some use feature vectors while others use matrices of car positions. This paper first compares a simple feature vector consisting of only queue sizes per incoming lane to a matrix of car positions. Then it investigates if knowledge can be transferred from a simple agent using the feature vector abstraction to a more complex agent that uses the position matrix abstraction.We find that training cannot be sped up by first training an agent with the feature vector abstraction and then reusing this Q-function to train an agent with the position matrix abstraction. The simple agent does not take considerably fewer samples to converge, and the total time needed to first train the simple agent and then transfer exceeds the time needed to train the complex agent from scratch.