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0. Referencehttps://arxiv.org/abs/1611.05725 PolyNet: A Pursuit of Structural Diversity in Very Deep NetworksA number of studies have shown that increasing the depth or width of convolutional networks is a rewarding approach to improve the performance of image recognition. In our study, however, we observed difficulties along both directions. On one hand, the purarxiv.org1. Introduction- 우선 해당 논..
0. Referencehttps://arxiv.org/abs/1611.05431 Aggregated Residual Transformations for Deep Neural NetworksWe present a simple, highly modularized network architecture for image classification. Our network is constructed by repeating a building block that aggregates a set of transformations with the same topology. Our simple design results in a homogeneous, mularxiv.org1. WRN- 해당 논문은, Wild Residua..
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0. Referencehttps://arxiv.org/abs/1610.02357 Xception: Deep Learning with Depthwise Separable ConvolutionsWe present an interpretation of Inception modules in convolutional neural networks as being an intermediate step in-between regular convolution and the depthwise separable convolution operation (a depthwise convolution followed by a pointwise convolution).arxiv.org1. Inception Hypothesis- 우선..
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0. Referencehttps://arxiv.org/abs/1608.06993https://www.youtube.com/watch?v=fe2Vn0mwALI&list=PLlMkM4tgfjnJhhd4wn5aj8fVTYJwIpWkS&index=29 Densely Connected Convolutional NetworksRecent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In ..
