Visualizing and Understanding Convolutional Networks

Slides:

Advertisements

Similar presentations

Classification spotlights

Advertisements

ImageNet Classification with Deep Convolutional Neural Networks

Presented by: Mingyuan Zhou Duke University, ECE September 18, 2009

Unsupervised Learning With Neural Nets Deep Learning and Neural Nets Spring 2015.

AN ANALYSIS OF SINGLE- LAYER NETWORKS IN UNSUPERVISED FEATURE LEARNING [1] Yani Chen 10/14/

Convolutional Neural Nets

Spatial Pyramid Pooling in Deep Convolutional

Overview of Back Propagation Algorithm

Building high-level features using large-scale unsupervised learning Anh Nguyen, Bay-yuan Hsu CS290D – Data Mining (Spring 2014) University of California,

Fully Convolutional Networks for Semantic Segmentation

Deep Convolutional Nets

Feedforward semantic segmentation with zoom-out features

Object Recognizing. Deep Learning Success in 2012 DeepNet and speech processing.

Lecture 3b: CNN: Advanced Layers

Deep Learning Overview Sources: workshop-tutorial-final.pdf

Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition arXiv: v4 [cs.CV(CVPR)] 23 Apr 2015 Kaiming He, Xiangyu Zhang, Shaoqing.

CNN architectures Mostly linear structure

Big data classification using neural network

Deep Learning for Dual-Energy X-Ray

Convolutional Neural Network

The Relationship between Deep Learning and Brain Function

CS 6501: 3D Reconstruction and Understanding Convolutional Neural Networks Connelly Barnes.

Deep Learning Amin Sobhani.

Computer Science and Engineering, Seoul National University

Convolutional Neural Fabrics by Shreyas Saxena, Jakob Verbeek

Krishna Kumar Singh, Yong Jae Lee University of California, Davis

Jure Zbontar, Yann LeCun

Applications of Deep Learning and how to get started with implementation of deep learning Presentation By : Manaswi Advisor : Dr.Chinmay.

Matt Gormley Lecture 16 October 24, 2016

CSCI 5922 Neural Networks and Deep Learning: Convolutional Nets For Image And Speech Processing Mike Mozer Department of Computer Science and Institute.

Lecture 24: Convolutional neural networks

Journal of Vision. 2017;17(4):9. doi: / Figure Legend:

Combining CNN with RNN for scene labeling (segmentation)

Spring Courses CSCI 5922 – Probabilistic Models (Mozer) CSCI Mind Reading Machines (Sidney D’Mello) CSCI 7000 – Human Centered Machine Learning.

Dhruv Batra Georgia Tech

ECE 6504 Deep Learning for Perception

Lecture 5 Smaller Network: CNN

CAMELYON16 Challenge Matt Berseth, NLP Logix Jacksonville FL

Convolution Neural Networks

Efficient Deep Model for Monocular Road Segmentation

Deep Belief Networks Psychology 209 February 22, 2013.

Unsupervised Learning and Autoencoders

Dipartimento di Ingegneria «Enzo Ferrari»

CS6890 Deep Learning Weizhen Cai

Machine Learning: The Connectionist

Non-linear classifiers Neural networks

Human-level control through deep reinforcement learning

Computer Vision James Hays

CNNs and compressive sensing Theoretical analysis

Convolutional Neural Networks

Introduction to Neural Networks

Image Classification.

Towards Understanding the Invertibility of Convolutional Neural Networks Anna C. Gilbert1, Yi Zhang1, Kibok Lee1, Yuting Zhang1, Honglak Lee1,2 1University.

Very Deep Convolutional Networks for Large-Scale Image Recognition

A Proposal Defense On Deep Residual Network For Face Recognition Presented By SAGAR MISHRA MECE

Neural Networks Geoff Hulten.

Lecture: Deep Convolutional Neural Networks

Forward and Backward Max Pooling

Designing Neural Network Architectures Using Reinforcement Learning

Deep Learning Some slides are from Prof. Andrew Ng of Stanford.

Mihir Patel and Nikhil Sardana

ImageNet Classification with Deep Convolutional Neural Networks

CSCI 5922 Neural Networks and Deep Learning: Convolutional Nets For Image And Speech Processing Mike Mozer Department of Computer Science and Institute.

CSC 578 Neural Networks and Deep Learning

CIS 519 Recitation 11/15/18.

Department of Computer Science Ben-Gurion University of the Negev

Deep Object Co-Segmentation

Natalie Lang Tomer Malach

DRC with Deep Networks Tanmay Lagare, Arpit Jain, Luis Francisco,

VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION

Presentation transcript:

Visualizing and Understanding Convolutional Networks By Patrapee Pongtana Credit: Matthew D. Zeiler, Rob Fergus Dept. of Computer Science, Courant Institute, New York University

Background of Convolutional Neural Networks Previously, Alex Krizhevsky has achieved error rate as low as 16.4% on Top-5 Test Architecture consists of 5 convolutional layers and 3 fully connected layers Krizhevsky utilized A large dataset that consists of millions of labeled examples A powerful GPU to train a large model A dropout method to regularize the better model Top 5 is simply a score that predicts the top 5 labels output. The 5 ones that has highest prediction

Motivation To observe each layer’s highest activation on feature maps To map these highest activation location back to input pixel space To learn what machine learns Same filter + same switches

Multi Layered Deconvolutional Network Performed under unsupervised learning Max-pooling MUST be able to reversed back The convolutional during reconstruct will perform conv transpose Unsupervised learning: Regardless of label; Input and reconstruct and keep top-level features sparse No optimization (?) Unsupervised because it does not learn anything new because convnet already learned the data already

Reconstruction Switches

Feature Map Top-9 Patches

Top-9 Highest of Activation from validation set

Top-9 Highest of Activation from validation set

Top-9 Highest of Activation from validation set

Top-9 Highest of Activation from validation set Layer 4

Matt Zeiber’s Architecture

Result

Conclusion The visualization of how machine learn helps us improve the performance by exploring the key location of each pixel E.g. We can know which hidden layer will be able to distinguish between human face and a dog face The intuition of this research is to explain “how” and “what” hidden layers (conv layers) learn features with given filters