神经网络已经在很多场景下表现出了很好的识别能力,但是缺乏解释性一直所为人诟病.<Grad-CAM:Visual Explanations from Deep Networks via Gradient-based Localization>这篇论文基于梯度为其可解释性做了一些工作,它可以显著描述哪块图片区域对识别起了至关重要的作用,以热度图的方式可视化神经网络的注意力.本博客主要是基于pytorch的简单工程复现.原文见这里,本代码基于这里. 1 import torch 2 import t…

目录 Grad-CAM:Visual Explanations from Deep Networks via Gradient-based Localization 1.Abstract 2.Introduction 3.Approach 4.Evaluating Localization 4.1. Weakly-supervised Localization 4.2 Weakly-supervised Segmentation 5.Evaluating Visualizations 5.1 E…

SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks 2019-04-02 12:44:36 Paper:https://arxiv.org/pdf/1812.11703.pdf Project:https://lb1100.github.io/SiamRPN++ 1. Background and Motivation: 与 CVPR 2019 的另一篇文章 Deeper and Wider Siames…

论文笔记之:Action-Decision Networks for Visual Tracking with Deep Reinforcement Learning  2017-06-06  21:43:53  这篇文章的 Motivation 来自于 MDNet: 本文所提出的 framework 为:…

前言 1.理论知识:UFLDL教程.Deep learning:十六(deep networks) 2.实验环境:win7, matlab2015b,16G内存,2T硬盘 3.实验内容:Exercise: Implement deep networks for digit classification.利用深度网络完成MNIST手写数字数据库中手写数字的识别.即:用6万个已标注数据(即:6万张28*28的图像块(patches)),作为训练数据集,然后把它输入到栈式自编码器中,它的第一层自编码器…

Exercise: Implement deep networks for digit classification 习题链接:Exercise: Implement deep networks for digit classification stackedAEPredict.m function [pred] = stackedAEPredict(theta, inputSize, hiddenSize, numClasses, netconfig, data) % stackedAEPre…

Overview In the previous sections, you constructed a 3-layer neural network comprising an input, hidden and output layer. While fairly effective for MNIST, this 3-layer model is a fairly shallow network; by this, we mean that the features (hidden lay…

目录 引 主要内容 定理1 Claim 1 Claim 2 定理2 证明 定理1的证明 Claim 1 的证明 Kronecker product (克罗内克积) Theorem 2 的证明 代码 Arora S, Cohen N, Hazan E, et al. On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization[J]. arXiv: Learning, 2018. 引 我很喜欢…

Initialization of deep networks 24 Feb 2015Gustav Larsson As we all know, the solution to a non-convex optimization algorithm (like stochastic gradient descent) depends on the initial values of the parameters. This post is about choosing initializati…

(一)Highway Networks 与 Deep Networks 的关系 理论实践表明神经网络的深度是至关重要的,深层神经网络在很多方面都已经取得了很好的效果,例如,在1000-class ImageNet数据集上的图像分类任务通过利用深层神经网络把准确率从84%提高到了95%,然而,在训练深层神经网络的时候却是非常困难的,神经网络的层数越多,存在的问题也就越多(例如大家熟知的梯度消失.梯度爆炸问题,下文会详细讲解).训练起来也就是愈加困难,这是一个公认的难题. 2015年由Rupesh…