3 accepted papers
Today's state of the art visual navigation agents typically consist of large deep learning architectures trained end to end. Such models offer little to no interpretability about the skills learned by the agent or the actions taken by it in response to its environment. While past works have explored…
In this paper, we tackle an open research question in transfer learning, which is selecting a model initialization to achieve high performance on a new task, given several pre-trained models. We propose a new highly efficient and accurate approach based on duality diagram similarity (DDS) between de…
Transfer learning is widely used in deep neural network models when there are few labeled examples available. The common approach is to take a pre-trained network in a similar task and finetune the model parameters. This is usually done blindly without a pre-selection from a set of pre-trained model…