0. Referencehttps://arxiv.org/abs/1409.1556 Very Deep Convolutional Networks for Large-Scale Image RecognitionIn this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3xarxiv.org1. Introduction- 본 논문에선..
0.Referencehttps://arxiv.org/abs/1312.4400 Network In NetworkWe propose a novel deep network structure called "Network In Network" (NIN) to enhance model discriminability for local patches within the receptive field. The conventional convolutional layer uses linear filters followed by a nonlinear activation functionarxiv.org1. Introduction- CNN은 ConVLayer와 Pooling Layer가 번갈아가며 구성된다.- ConVLayer에선..
0. Referencehttps://arxiv.org/abs/1311.2901 Visualizing and Understanding Convolutional NetworksLarge Convolutional Network models have recently demonstrated impressive classification performance on the ImageNet benchmark. However there is no clear understanding of why they perform so well, or how they might be improved. In this paper we address botharxiv.org 1. Introduction- 논문에서 ConvNets의 성능 향..
0. Referencehttps://papers.nips.cc/paper_files/paper/2012/hash/c399862d3b9d6b76c8436e924a68c45b-Abstract.html ImageNet Classification with Deep Convolutional Neural NetworksRequests for name changes in the electronic proceedings will be accepted with no questions asked. However name changes may cause bibliographic tracking issues. Authors are asked to consider this carefully and discuss it with ..
