HD-MTL: MTL: Hierarchical Deep Multi Multi-Task Task Learning for Large-Scale Large Visual Recognition
Abstract: In this paper, a hierarchical deep multi multi-task learning (HD-MTL) MTL) algorithm is developed to support large--scale scale visual recognition (e.g., recognizing thousands or even tens of thousands of atomic object classes automatically). To achieve more effective accomplishment plishment of the coarse coarse-to-fine fine tasks for hierarchical visual recognition, multiple sets of deep features are first extracted from the different layers of deep convolutional neural networks (deep CNNs). A visual tree is then learned by assigning the visual visually-similar similar atomic object classes with similar learning complexities into the same group, and it can provide a good environment for identifying the inter-related related learning tasks automatically. By leveraging the interinter task relatedness (inter-class class similaritie similarities) s) to learn more discriminative groupgroup specific deep representations, our deep multi multi-task task learning algorithm can achieve the global optimum easily and obtain more discriminative node classifiers for distinguishing the visually--similar atomic object classes (in the same group) effectively. Our HD-MTL MTL algorithm can control the inter inter-level level error propagation effectively by using an end end-to-end end approach for jointly learning more representative deep CNNs (for image representation) and more discriminative tree classifier ifier (for large large-scale scale visual recognition) and updating them simultaneously. Our incremental deep learning algorithms can effectively adapt both the deep CNNs and the tree classifier to the new training images and the