26,205 research outputs found
Unifying and Merging Well-trained Deep Neural Networks for Inference Stage
We propose a novel method to merge convolutional neural-nets for the
inference stage. Given two well-trained networks that may have different
architectures that handle different tasks, our method aligns the layers of the
original networks and merges them into a unified model by sharing the
representative codes of weights. The shared weights are further re-trained to
fine-tune the performance of the merged model. The proposed method effectively
produces a compact model that may run original tasks simultaneously on
resource-limited devices. As it preserves the general architectures and
leverages the co-used weights of well-trained networks, a substantial training
overhead can be reduced to shorten the system development time. Experimental
results demonstrate a satisfactory performance and validate the effectiveness
of the method.Comment: To appear in the 27th International Joint Conference on Artificial
Intelligence and the 23rd European Conference on Artificial Intelligence,
2018. (IJCAI-ECAI 2018
Richly Activated Graph Convolutional Network for Action Recognition with Incomplete Skeletons
Current methods for skeleton-based human action recognition usually work with
completely observed skeletons. However, in real scenarios, it is prone to
capture incomplete and noisy skeletons, which will deteriorate the performance
of traditional models. To enhance the robustness of action recognition models
to incomplete skeletons, we propose a multi-stream graph convolutional network
(GCN) for exploring sufficient discriminative features distributed over all
skeleton joints. Here, each stream of the network is only responsible for
learning features from currently unactivated joints, which are distinguished by
the class activation maps (CAM) obtained by preceding streams, so that the
activated joints of the proposed method are obviously more than traditional
methods. Thus, the proposed method is termed richly activated GCN (RA-GCN),
where the richly discovered features will improve the robustness of the model.
Compared to the state-of-the-art methods, the RA-GCN achieves comparable
performance on the NTU RGB+D dataset. Moreover, on a synthetic occlusion
dataset, the performance deterioration can be alleviated by the RA-GCN
significantly.Comment: Accepted by ICIP 2019, 5 pages, 3 figures, 3 table
Pseudorandom States, Non-Cloning Theorems and Quantum Money
We propose the concept of pseudorandom states and study their constructions,
properties, and applications. Under the assumption that quantum-secure one-way
functions exist, we present concrete and efficient constructions of
pseudorandom states. The non-cloning theorem plays a central role in our
study---it motivates the proper definition and characterizes one of the
important properties of pseudorandom quantum states. Namely, there is no
efficient quantum algorithm that can create more copies of the state from a
given number of pseudorandom states. As the main application, we prove that any
family of pseudorandom states naturally gives rise to a private-key quantum
money scheme.Comment: 20 page
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