Tractable Approximations for Achieving Higher Model Efficiency in Computer Vision

The 2010s have seen the first large-scale successes of computer vision" in the wild", paving the way for industrial applications. Thanks to the formidable increase of processing power in consumer electronics, convolutional neural networks have led the way in this revolution. With enough supervision, these models have proven able to surpass human accuracy on many vision tasks. However, rather than focusing exclusively on accuracy, it is increasingly important to design algorithms that operate within the bounds of a computational budget-in terms of latency, memory, or energy consumption. The adoption of vision algorithms in time-critical decision systems (such as autonomous driving) and in edge computing (\eg {} in smartphones) makes this quest for efficiency a central challenge in machine learning research. How can the optimization of existing models be improved, in order to reach higher accuracy without affecting the processing requirements? Alternatively, can we search for models that fit the processing requirements while improving the accuracy on the task? In this thesis, we consider both of these questions, which are two sides of the same coin. On one hand, we develop novel methods for learning model parameters in a supervised fashion, improving the accuracy on the target task without affecting the efficiency of these models at test-time. On the other, we study the problem of model search, where the model itself must be selected among a family of models in order to achieve satisfactory accuracy under the resource constraints. Chapter 3 introduces the probably submodular framework for learning the weights of pairwise random …