@joel95
deeplearning
Nov 6, 2022 · 3 min read · SENet paper: https://arxiv.org/abs/1709.01507 The paper I'm going to read this time is SENet, Squeeze-and-Excitation Networks. SENet is a model that increases performance by focusing on interaction between channels. Interactions between channels can ...
Join discussionOct 30, 2022 · 2 min read · MobileNet V1: https://arxiv.org/abs/1704.04861 In the early days of CNN, it is composed of a series of convolution-pooling, simply increasing the number of channels. The most intuitive structure is easy to understand and implement. However, the conv...
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Oct 25, 2022 · 4 min read · ResNet paper : https://arxiv.org/abs/1512.03385 Today, we're going to talk about ResNet, which was introduced by the Microsoft team in Deep Residual Learning for Image Recognition ResNet introduced a methodically novel concept rather than a mathemati...
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Oct 18, 2022 · 3 min read · VGG paper : https://arxiv.org/abs/1409.1556 Abstarct The VGG paper investigates the accuracy pattern while increasing the depth of convolutional networts. The model is a 16-19 depth model using 3x3 convolution layers continuously. In fact, this model...
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Oct 17, 2022 · 3 min read · YOLO V3 paper : https://arxiv.org/abs/1804.02767?e05802c1_page=1 YOLOv3 is a lot like YOLOv2. I'm just going to write down the difference. 1. Abstract Updated from previous versions (YOLO v1, v2), YOLO v3 performs better than before. Increased accu...
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