SeyedHamed RahmaniKhezri

Presentation for the Graduate Course CMPT 985: Computational Photography and Image Manipulation

In order to do so, we used the ideas influenced by the active learning. We defined an oracle to provide a label for the question that is asked about an image. This oracle is an image captioning network, that given an image as its input, generates a sentence that describes the objects that are visible in that image. We use a semantic similarity calculator, in order to connect the result of the image captioning model and interpret that to become a potential label for the visual question answering task.

We considered a multi-InP scenario in which different levels of reliability with different costs are offered to the network operator. On the other hand, we considered multiple incoming services with different reliability requirements. For DQN-agent, we defined the state set, action set, reward and memory considering the objective of the NO which is maximizing the admission ratio while minimizing the placement cost. Using simulations, we showed that the NO could learn how to effectively use the resources of the InPs for various services in different states in a way that the admission ratio is maximized and placement cost is minimized.