In this paper, a hierarchical deep multi-task learning (HD-MTL) algorithm is developed to support large-scale visual recognition (e.g., recognizing thousands or even tens of thousands of atomic object classes automatically). To achieve more effective accomplishment of the coarse-to-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 visually-similar atomic object classes with similar learning complexities into the same group, and it can provide a good environment for identifying the inter-related learning tasks automatically. By leveraging the inter-task relatedness (inter-class similarities) to learn more discriminative group-specific deep representations, our deep multi-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 algorithm can control the inter-level error propagation effectively by using an end-to-end approach for jointly learning more representative deep CNNs (for image representation) and more discriminative tree classifier (for large-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 new object classes. Our experimental results have demonstrated that our HD-MTL algorithm can achieve very competitive results on both the accuracy rates and the computational efficiency for large-scale visual recognition.

https://ieeexplore.ieee.org/ielaam/83/7864504/7849143-aam.pdf

HD-MTL: Hierarchical Deep Multi-Task Learning for Large-Scale Visual Recognition

In object classification tasks from digital photographs, multiple categories are considered for annotation. Some of these visual concepts may have semantic relations and can appear simultaneously in images. Although taxonomical relations and co-occurrence structures between object categories have been studied, it is not easy to use such information to enhance performance of object classification. In this paper, we propose a novel multi-task learning procedure which extracts useful information fromthe classifiers for the other categories. Our approach is based on non-sparse multiple kernel learning (MKL) which has been successfully applied to adaptive feature selection for image classification. Experimental results on PASCAL VOC 2009 data show the potential of our method.

/pdf/multi-task-learning-via-non-sparse-multiple-kernel-learning-24m03h68wt.pdf

Multi-task learning via non-sparse multiple kernel learning

Many kernel based methods for multi-task learning have been proposed, which leverage relations among tasks to enhance the overall learning accuracies. Most of the methods assume that the learning tasks share the same kernel [e.g., 13], which could limit their applications because in practice different tasks may need different kernels. In this paper, we consider utilizing multiple kernels for multiple tasks. The main challenge of introducing multiple kernels into multiple tasks is that functions from different Reproducing Kernel Hilbert Spaces (RKHSs) are not comparable, making it difficult to exploit relations among tasks. This paper addresses the challenge by defining the problem in the Square Integrable Space (SIS). Specially, it proposes a kernel based method which makes use of a regularization term defined in the SIS to represent task relations. We prove a new representer theorem for the proposed approach in SIS. We further derive a practical method for solving the learning problem and conduct consistency analysis of the method. We discuss the relations between our method and the existing method by showing the inequality relation between the two regularization terms in the two methods. We also give an SVM based implementation of our method for multi-label classification. Experiments on an artificial example and three real-world data sets show significant improvements of the proposed method over existing methods.

/pdf/multi-task-learning-multiple-kernels-for-multiple-tasks-3m1xpvzd90.pdf

Multi-Task Learning: Multiple Kernels for Multiple Tasks

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Multi-Task Learning: Multiple Kernels for Multiple Tasks