Deep learning veteran Yann LeCun detailed convolutional neural network

Deep learning veteran Yann LeCun detailed convolutional neural network

The author of this article: Li Zun

2016-08-23 18:39

This article co-compiles: Blake, Ms Fenny Gao

Lei Feng Net (public number: Lei Feng net) Note: convolutional Neural Networks (convolutional neural network) is a feedforward neural network, its artificial neurons can respond to a part of the coverage of the surrounding cells, for large-scale image processing has excellent performance.

Yann LeCun was born in France and was a post-doctoral researcher at the University of Toronto following the Geoffrey Hinton, the founder of Deep Learning. As early as the late 1980s, Yann LeCun, a researcher at Bell Labs, presented convolutional networking techniques and demonstrated how to use it to dramatically improve handwriting recognition. At the beginning of the last century, when neural networks fell out of favor, Yann LeCun was one of the few scientists to insist on. He became a professor at New York University in 2003 and has led the development of deep learning, currently serving on the Facebook Fair lab. This paper is a presentation ppt of Yann LeCun for convolutional Neural Networks (convolutional neural network).

Yann LeCun (Information Science and Computer Science) (2015-2016) convnets attempt Process

First convolutional neural network Model (University of Toronto) (LeCun 88,89)

A total of 320 examples of training using the inverse propagation algorithm

Convolution with stride (sub-sample)

Tightly connected pooling process

The first "real" convolutional neural network model established at Bell Labs (LeCun et al)

Using the inverse propagation algorithm to train

USPS coded numbers: 7,300 sessions, 2000 tests

Convolution with Stride

Tightly connected pooling process

convolutional Neural Network (Vintage 1990)

Filter-Hyperbolic tangent--pooling--filter-hyperbolic tangent--pooling

Multi-convolutional networks Architecture

The structure of convolutional neural networks

The convolution operation process of convolutional neural networks is as follows:

The input image is non-linear convolution via three trained filter banks, the feature map is generated at each layer after convolution, then the four pixels in the feature map are summed, weighted, and biased, and in this process the pixels are pooled in the pool layer and the output value is finally obtained.

The overall structure of convolutional neural networks:

Normalization--Filter bank--nonlinear computation--pooling

Normalization: Distortion of image whitening treatment (optional)

Subtraction: Average removal, high-pass filter processing

Division operations: Local contrast normalization, variance normalization

Filter banks: Dimension expansion, mapping

Non-linear: sparse, saturated, side-suppressed

Distillation, wise contraction of ingredients, hyperbolic tangent, etc.

Pooling: aggregation of spatial or feature types

maximizing, LP norm, logarithmic probability

LeNet5

A simplified model of convolutional neural networks

MNIST (LeCun 1998)

Phase 1: Filter Banks--extrusion--maximum pooling

Phase 2: Filter Banks--extrusion--maximum pooling

Phase 3: Standard 2-layer MLP

multi-feature recognition (Matan et al 1992)

Each layer is a convolution layer

Single feature recognizer--SDNN

sliding window convolution neural network + weighted finite state machine Application

the application range of convolutional neural network

The signal appears as an array of (multiple degrees)

A signal with a strong local correlation

A signal that features can appear anywhere

A signal that a target is not altered by translation or distortion.

One dimensional convolutional neural network: Timing signal, text

Text classification

Music genre classification

Acoustic models for speech recognition

Time series prediction

Two dimensional convolutional neural networks: image, time-frequency characterization (voice and audio)

Object detection, positioning, identification

Three-dimensional convolutional neural networks: Video, stereo images, tomography

Video Recognition/understanding

Biomedical image analysis

hyperspectral image Analysis

Human Face Detection (Vaillant et al.93, 94)

convolutional neural Networks for large image detection

Multi-scale Thermal map

Non-maximum suppression of candidate images

6-second sparse for 256x256 images

the state of the art result of human face detection

Application of convolutional neural network in biological image cutting

Bio-Image Cutting (Ning et al. Ieee-tip 2005)

Using convolutional neural networks to mark pixels in a large background

convolutional neural networks have a pixel window that marks the central pixel

Use a conditional random field to clear

Version 3D connector (Jain et al.2007)

scene parsing/tagging

scene parsing/tagging: Multi-scale convolutional neural network architecture

Each output value corresponds to a large input background

46x46 full pixel window; 92X92 1/2 pixel window; 182x182 1/4 pixel window

[7x7 convolution Operation]->[2x2 Pooling]->[7x7 convolution operation]->[2x2 pooling]->[7x7 convolution operation]

Supervised training full-tagged Image

method: Select the main part by the Super Pixel region

Input image--hyper-pixel boundary parameter--hyper pixel boundary--the main part of the voting process via hyper-pixels--category and Region boundary alignment

Multi-scale convolutional networks-convolution network features (d=768 per pixel) Volume integration class--"soft" classification score

Scene analysis/tagging

No upfront processing

Frames-by-frame

Run a convolutional network on a 50ms frame on Vittex-6 FPGA hardware

But the transmission of features over Ethernet limits the performance of the system.

convolutional networks for Remote Adaptive Robot Vision (DARPA LAGR project 2005-2008)

Input image

Mark

Categorical output

very deep convolutional network architecture

Small cores, less secondary sampling (small secondary sampling)

Vgg

Googlenet

Resnet

object detection and positioning using convolutional networks

category + Positioning: multiple mobile windows

Applying a convolutional network with multiple sliding windows to an image

Important: Applying a convolutional network to a picture is very inexpensive

Just calculate the convolution of the entire image and copy the full connection layer

category + Positioning: sliding window + box return

Applying a convolutional network with multiple sliding windows to an image

For each window, predict a category and limit box parameters

Even if the goal is not fully contained in the Browse window, the convolutional network can guess what it considers the target to be.

Deep Face

Taigman et CVPR 2014

Queue

Convolutional Networks

Metric Learning

Facebook-Developed automatic tagging method

800 million photos per day

Posture Estimation and property recovery using convolutional networks

Posture Alignment Network for depth attribute model

Zhang et CVPR (Facebook AI)

character detection and posture estimation

Tompson,goroshin,jain,lecun,bregler et arxiv (2014)

monitoring convolutional Network drawing

Use convolutional networks to paint

Dosovitskyi et arxiv (1411:5,928)

monitoring convolutional Network drawing

Create Chair

Calculation of chairs with feature space

Global (end-to-end) learning: Energy Model

Input-convolution network (or other depth architecture)-Energy module (latent variable, output)-energy

So that each module in the system can be trained.

All modules are trained at the same time so that the global loss function can be optimized.

Includes feature extractor, recognizer, and front and back processor (image model).

Problem: Reverse propagation is skewed in the image model

deep convolutional networks (with other deep neural networks)

Training Sample: (Xi,yi) k=1 to K

Object function (Edge type loss = ReLU)

Map from NewScientist.com