分类与聚类的区别

no_speaking 2018-06-17 00:47
分类与聚类的区别
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pytorch应用之——纸币识别(一)

飞翔 2019-05-31 17:22
这里数据集一共有39620张,而且背景单一,所以纸币面值的识别不是一个很难的问题。我用resnet18(自己稍微改了一些结构,影响不大)去训练这个数据集,迭代24次可以达到99.96%的精度。

Identity Mappings in Deep Residual Networks

jixiaohui 2018-01-10 12:10
Deep residual networks have emerged as a family of extremely deep architectures showing compelling accuracy and nice convergence behaviors. In this paper, we analyze the propagation formulations behin

Rethinking the Inception Architecture for Computer Vision

辉仔 2018-01-10 11:10
Convolutional networks are at the core of most state-of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yieldin

Dynamic Routing Between Capsules

dwSun 2018-02-06 16:16
A capsule is a group of neurons whose activity vector represents the instantiation parameters of a specific type of entity such as an object or an object part. We use the length of the activity vector

Fully Convolutional Networks for Semantic Segmentation

辉仔 2018-01-10 10:46
Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, exceed the state-of-the-a

Deep Residual Learning for Image Recognition

jixiaohui 2018-01-10 11:15
Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly re

1吴恩达Meachine-Learing之监督学习和非监督学习

阿小庆 2018-06-24 17:11
介绍监督学习和非监督学习的区别。

One weird trick for parallelizing convolutional neural networks

辉仔 2018-01-10 10:26
I present a new way to parallelize the training of convolutional neural networks across multiple GPUs. The method scales significantly better than all alternatives when applied to modern convolutional

Learning Transferable Architectures for Scalable Image Recognition

jixiaohui 2018-01-10 15:36
Developing neural network image classification models often requires significant architecture engineering. In this paper, we attempt to automate this engineering process by learning the model architec
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