As deep learning applications continue to become more diverse, an interesting question arises: Can general problem solving arise from jointly learning several such diverse tasks? To approach this question, deep multi-task learning is extended in this paper to the setting where there is no obvious overlap between task architectures. The idea is that any set of (architecture,task) pairs can be decomposed into a set of potentially related subproblems, whose sharing is optimized by an efficient stochastic algorithm. The approach is first validated in a classic synthetic multi-task learning benchmark, and then applied to sharing across disparate architectures for vision, NLP, and genomics tasks. It discovers regularities across these domains, encodes them into sharable modules, and combines these modules systematically to improve performance in the individual tasks. The results confirm that sharing learned functionality across diverse domains and architectures is indeed beneficial, thus establishing a key ingredient for general problem solving in the future.
Parameters in two architectures are if they have some learnable tensor in common. An alignment across architectures implies how tasks are related, and how much they are related. The goal of DMTL is to improve performance across tasks through joint training of aligned architectures, exploiting inter-task regularities. In recent years, DMTL has been applied within areas such as vision @cite_62 @cite_2 @cite_52 @cite_5 @cite_45 @cite_26 , natural language @cite_46 @cite_36 @cite_60 @cite_67 @cite_0 , speech @cite_43 @cite_65 @cite_63 , and reinforcement learning @cite_61 @cite_23 @cite_25 . The rest of this section reviews existing DMTL methods, showing that none of these methods satisfy both conditions (1) and (2).
@cite_61 @cite_67 @cite_62 @cite_26 @cite_60 @cite_36 @cite_65 @cite_52 @cite_0 @cite_43 @cite_45 @cite_63 @cite_2 @cite_23 @cite_5 @cite_46 @cite_25
2900964459 2966182616 2891303672 2737590896
Multi-task learning (MTL) allows deep neural networks to learn from related tasks by sharing parameters with other networks. In practice, however, MTL involves searching an enormous space of possible parameter sharing architectures to find (a) the layers or subspaces that benefit from sharing, (b) the appropriate amount of sharing, and (c) the appropriate relative weights of the different task losses. Recent work has addressed each of the above problems in isolation. In this work we present an approach that learns a latent multi-task architecture that jointly addresses (a)--(c). We present experiments on synthetic data and data from OntoNotes 5.0, including four different tasks and seven different domains. Our extension consistently outperforms previous approaches to learning latent architectures for multi-task problems and achieves up to 15 average error reductions over common approaches to MTL. Multi-task learning (MTL) allows deep neural networks to learn from related tasks by sharing parameters with other networks. In practice, however, MTL involves searching an enormous space of possible parameter sharing architectures to find (a) the layers or subspaces that benefit from sharing, (b) the appropriate amount of sharing, and (c) the appropriate relative weights of the different task losses. Recent work has addressed each of the above problems in isolation. In this work we present an approach that learns a latent multi-task architecture that jointly addresses (a)–(c). We present experiments on synthetic data and data from OntoNotes 5.0, including four different tasks and seven different domains. Our extension consistently outperforms previous approaches to learning latent architectures for multi-task problems and achieves up to 15 average error reductions over common approaches to MTL. Multi-Task Learning (MTL) is appealing for deep learning regularization. In this paper, we tackle a specific MTL context denoted as primary MTL, where the ultimate goal is to improve the performance of a given primary task by leveraging several other auxiliary tasks. Our main methodological contribution is to introduce ROCK, a new generic multi-modal fusion block for deep learning tailored to the primary MTL context. ROCK architecture is based on a residual connection, which makes forward prediction explicitly impacted by the intermediate auxiliary representations. The auxiliary predictor's architecture is also specifically designed to our primary MTL context, by incorporating intensive pooling operators for maximizing complementarity of intermediate representations. Extensive experiments on NYUv2 dataset (object detection with scene classification, depth prediction, and surface normal estimation as auxiliary tasks) validate the relevance of the approach and its superiority to flat MTL approaches. Our method outperforms state-of-the-art object detection models on NYUv2 by a large margin, and is also able to handle large-scale heterogeneous inputs (real and synthetic images) and missing annotation modalities. This paper proposes a data-driven approach for image alignment. Our main contribution is a novel network architecture that combines the strengths of convolutional neural networks (CNNs) and the Lucas-Kanade algorithm. The main component of this architecture is a Lucas-Kanade layer that performs the inverse compositional algorithm on convolutional feature maps. To train our network, we develop a cascaded feature learning method that incorporates the coarse-to-fine strategy into the training process. This method learns a pyramid representation of convolutional features in a cascaded manner and yields a cascaded network that performs coarse-to-fine alignment on the feature pyramids. We apply our model to the task of homography estimation, and perform training and evaluation on a large labeled dataset generated from the MS-COCO dataset. Experimental results show that the proposed approach significantly outperforms the other methods.