Tangled Watermarks for Deep Neural Networks


Tangled Watermarks for Deep Neural Networks – This paper proposes a framework for learning dense Markov networks (MHN) over a large data set. MHN is a family of deep learning methods focusing on image synthesis over structured representations. Recent studies have evaluated three MHN architectures on a range of tasks: 1) text recognition, 2) text classification, and 3) face recognition. MHN models provide a set of outputs, that can be useful for learning a novel representation over images. However, it may take many tasks without good input data. Therefore, MHN model is a multi-task learning system. First, we learn MHN from data. We then use a mixture of both learned inputs and output outputs for learning MHN. Second, we use the same inputs in two different tasks, namely object detection and visual pose estimation.

A major challenge in neural machine translation (NMT) is to identify candidate words that are consistent with the word usage patterns in the input text. In this paper, we develop a novel technique in which the task of detecting the word phrase similarity is derived from an optimization-based inference algorithm. To evaluate this technique we conduct a detailed feasibility study. We show that the proposed approach achieves state-of-the-art performance on the COCO benchmark as well as the state-of-the-art performance of the KITTI and COCO datasets, for a total of ~3.7% and 3.8% respectively, respectively.

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Tangled Watermarks for Deep Neural Networks

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  • Efficiently Regularizing Log-Determinantal Point Processes: A General Framework and Completeness Querying Approach

    Learning 3D Object Proposals from Semantic Labels with Deep Convolutional Neural NetworksA major challenge in neural machine translation (NMT) is to identify candidate words that are consistent with the word usage patterns in the input text. In this paper, we develop a novel technique in which the task of detecting the word phrase similarity is derived from an optimization-based inference algorithm. To evaluate this technique we conduct a detailed feasibility study. We show that the proposed approach achieves state-of-the-art performance on the COCO benchmark as well as the state-of-the-art performance of the KITTI and COCO datasets, for a total of ~3.7% and 3.8% respectively, respectively.


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