Partial Least Squares: A Deep Space Odyssey - Jordão, Artur; Schwartz, William Robson (Anais Estendidos da Conference on Graphics, Patterns and Images (SIBGRAPI); 2021: Anais Estendidos da XXXIV Conference on Graphics, Patterns and ImagesDO - 10.5753/sibgrapi.est.2021.20014, 2021)
Modern visual pattern recognition models are based on deep convolutional networks. Such models are computationally expensive, hindering applicability on resource-constrained devices. To handle this problem, we propose three strategies. The first removes unimportant structures (neurons or layers) of convolutional networks, reducing their computational cost. The second inserts structures to design architectures automatically, enabling us to build high-performance networks. The third combines multiple layers of convolutional networks, enhancing data representation at negligible additional cost. These strategies are based on Partial Least Squares (PLS) which, despite promising results, is infeasible on large datasets due to memory constraints. To address this issue, we also propose a discriminative and lowcomplexity incremental PLS that learns a compact representation of the data using a single sample at a time, thus enabling applicability on large datasets. We assess the effectiveness of our approaches on several convolutional architectures and computer vision tasks, which include image classification, face verification and activity recognition. Our approaches reduce the resource overhead of both convolutional networks and Partial Least Squares, promoting energyand hardware-friendly models for the academy and industry scenarios. Compared to state-of-theart methods for the same purpose, we obtain one of the best trade-offs between predictive ability and computational cost.