Convolutional Features
Found 4 free book(s)
Densely Connected Convolutional Networks
openaccess.thecvf.comcially, in contrast to ResNets, we never combine features through summation before they are passed into a layer; in-stead, we combine features by concatenating them. Hence, the ℓth layer has ℓ inputs, consisting of the feature-maps of all preceding convolutional blocks. Its own feature-maps arepassedontoallL−ℓsubsequentlayers ...
![and Super-Resolution arXiv:1603.08155v1 [cs.CV] 27 Mar 2016](/cache/preview/2/d/c/1/c/a/1/5/thumb-2dc1ca15e82fc13a292da02b4114d430.jpg)
and Super-Resolution arXiv:1603.08155v1 [cs.CV] 27 Mar 2016
arxiv.orglevel features extracted from a convolutional network. Images can be generated to maximize class prediction scores [7,8] or individual features [8] in order to under-stand the functions encoded in trained networks. Similar optimization techniques can also be used to generate high-con dence fooling images [18,19].
Image Inpainting for Irregular Holes Using Partial ...
openaccess.thecvf.comconvolutional layers, the raw features of noise or wrong hole initialization values in the encoder stage will propagate to the decoder stage. Our work also does not depend on placeholder values in the hole regions, but we also aim to achieve. Image …
Character-level Convolutional Networks for Text Classification
papers.nips.cc2 Character-level Convolutional Networks In this section, we introduce the design of character-level ConvNets for text classification. The de-sign is modular, where the gradients are obtained by back-propagation [27] to perform optimization. 2.1 Key Modules The main component is the temporal convolutional module, which simply computes a 1-D convo-