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Pretreatment: Mean removal;whitening (ZCA)
Enhanced generalization capability: Data augmentation;weight regularization; adding noise to the network, including dropout,dropconnect,stochastic pooling.
Dropout: The output of some neurons in the fully connected layer is randomly set to 0 at the full connection layer only.
Dropconnect: Also only used on the full-connection layer, Random binary mask on weights.
Stochastic Pooli
image recognition:Real life picture resolution is much higher than 32*32, and the resolution is not fixed; there are many kinds of objects in real life, and there is not only one object in each image;
2. ImageNet was led by Stanford Professor Li Feifei (Feifei Li) to organize the database, more close to the real life environment. The imagenet dataset has more than 14 million images covering more than 20,000 categories, of which more than millions of
0-Background
The so-called style conversion is based on a content image and a style image, merging the two, creating a new image that combines both contents and style.The required dependencies are as follows:
Import OS
import sys
import scipy.io
import scipy.misc
import Matplotlib.pyplot as Plt
from Matplotlib.pyplot import imshow from
PIL import Image from
nst_utils import *
import NumPy as NP
import te Nsorflow as TF
%matplotlib inline
1-transfer Learning
Migration learning is the applicat
visual comprehension of convolutional neural networks The
first to suggest a visual understanding of convolutional neural Networks is Matthew D. Zeiler in the visualizing and understanding convolutional Networks.
The following two blog posts can help you understand this a
. We use the cublas. lib and curand. Lib libraries. One is matrix calculation and the other is random number generation. I applied for all the memory I needed at one time. After the program started running, there was no data exchange between the CPU and GPU. This proved to be very effective. The program performance is about dozens of times faster than the original C language version (if the network is relatively large, it can reach a speed-up ratio of
I. Documentation names and authorsconvolutional neural Networks at Constrained time COST,CVPR two. Reading timeJune 30, 2015Three. Purpose of the documentThe author hopes to improve the accuracy of CNN by modifying the model depth and the parameters of the convolution template, while maintaining the computational complexity. Through a lot of experiments, the author finds the importance of different parameters in the
scientists have contributed significantly to the success of convolutional networks?There is no doubt that the neuro-cognitive machine (Neocognitron) proposed by Japanese scholar Kunihiko Fukushima has enlightening significance. Although the early forms of convolutional networks (Convnets) did not contain too many Neocognitron, the versions we used (with pooling layers) were affected.This is a demonstration
large number of outstanding academics joining the deep neural network, especially the Bengio research group at the University of Montreal and the NG Research Group at Stanford University. From the analysis of the proposed model, an important contribution of the Bengio research group is to propose a deep learning network
affected.This is a demonstration of the mutual connection between the middle layer and the layers of the neuro-cognitive machine. Fukushima K. (1980) in the neuro-cognitive machine article, the self-organizing neural network model of pattern recognition mechanism is not affected by the change of position.Can you recall the "epiphany" moments or breakthroughs that occurred in the early days of
set, the KL distance is the indicator that describes the diversity, thus reducing the amount of computation. Traditional deep learning will need to do before the training of data enhancement, each sample is equal; This article contains some data enhancement not only does not play a good role, but brings the noise, it needs to do some processing, but also some of the data does not need to be enhanced, which reduces noise and saves calculation.
Qa
Q: Why did the active learning not b
Wang, Min, Baoyuan Liu, and Hassan Foroosh. "Factorized convolutional neural Networks." ArXiv preprint (2016).
This paper focuses on the optimization of the convolution layer in the deep network, which has three unique features:-Can be trained directly . You do not need to train the original model first, then use the sparse, compressed bits and so on to compress.
of the "object" in the "the position with the maximum score
Use a cost function this can explicitly model multiple objects present in the image.
Because there may be many objects in the graph, the multi-class classification loss is not applicable. The author sees this task as multiple two classification questions, loss function and classification score as followsTrainingMuti-scale TestExperimentClassification
MAP on VOC test: +3.1% compared with [56]
MAP on VOC test: +7.
1, IntroductionDL solves VO problem: End-to-end vo with RCNN2. Network structureA.CNN based Feature ExtractionThe paper uses the Kitti data set.The CNN section has 9 convolutional layers, with the exception of CONV6, the other convolutional layers are connected to 1 layers of relu, and there are 17 layers.B, RNN based sequential modellingRNN is different from CNN
Minimalist notes Deepid-net:object detection with deformable part Based convolutional Neural Networks
Paper Address Http://www.ee.cuhk.edu.hk/~xgwang/papers/ouyangZWpami16.pdf
This is the CUHK Wang Xiaogang group 2017 years of a tpami, the first hair in the CVPR2015, increased after the experiment to cast the journal, so the contrast experiment are some alexnet,googlenet and other early
ImageNet classification with deep convolutional neural Networks reading notes(2013-07-06 22:16:36) reprint
Tags: deep_learning imagenet Hinton
Category: machine learning
(after deciding to read a paper each time, the notes are recorded on the blog.) )This article, published in NIPS2012, is Hinton and his students are using deep learning in response to doubts about deep learn
http://m.blog.csdn.net/blog/wu010555688/24487301This article has compiled a number of online Daniel's blog, detailed explanation of CNN's basic structure and core ideas, welcome to exchange.[1] Deep Learning Introduction[2] Deep Learning training Process[3] Deep learning Model: the derivation and implementation of CNN convolution neural network[4] Deep learning Model: the reverse derivation and practice of
alexnet Summary Notes
Thesis: "Imagenet classification with Deep convolutional neural"
1 Network Structure
The network uses the logic regression objective function to obtain the parameter optimization, this network structure as shown in Figure 1, a total of 8 layer
Original page: Visualizing parts of convolutional neural Networks using Keras and CatsTranslation: convolutional neural network Combat (Visualization section)--using Keras to identify cats
It is well known, that convolutional
neurons are active, only a very small fraction will be active, the different layers of neurons can not be fully connected. In the back of 5.5.6, we will see an example of the sparse network structure used by convolutional neural networks.We can naturally design a more complex network structure, but in general we have
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