What do filters of Convolution Neural Network learn?

CodeEmporium · Beginner ·🧬 Deep Learning ·5y ago

Key Takeaways

This video explores what Convolution Neural Network filters learn, referencing research papers and discussing human interpretability of CNN filters, with topics including image classification, bilinear interpolation, and activation functions.

Original Description

What do Convolution Neural Network filters really learn? Are they human interpretable? Please subscribe to keep me alive: https://www.youtube.com/c/CodeEmporium?sub_confirmation=1 SPONSOR Kite is a free AI-powered coding assistant that will help you code faster and smarter. The Kite plugin integrates with all the top editors and IDEs to give you smart completions and documentation while you’re typing. I've been using Kite. Love it! https://www.kite.com/get-kite/?utm_medium=referral&utm_source=youtube&utm_campaign=codeemporium&utm_content=description-only TIMESTAMPS 0:00 - Personal Note 0:29 - Introduction 2:50 - Pass 1: How do Humans classify Images? 3:57 - Pass 2: How do networks classify Images? 6:55 - Bilinear Interpolation 9:00 - Activation Function (the mask) 10:25 - Intersection over Union (IoU) 11:20 - Interesting findings from main paper REFERENCES [1] The main paper that talks about how object detectors come about from image classifiers: https://arxiv.org/abs/2009.05041 [2] More on upsampling techniques like Bilinear interpolation and how it compares to other techniques: https://www.quora.com/What-is-the-difference-between-Deconvolution-Upsampling-Unpooling-and-Convolutional-Sparse-Coding [3] Resizing images with bilinear interpolation @Computerphile’s video: https://www.youtube.com/watch?v=AqscP7rc8_M [4] Places365 dataset: http://places2.csail.mit.edu/explore.html
Watch on YouTube ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Playlist

Uploads from CodeEmporium · CodeEmporium · 50 of 60

← Previous Next →
1 Linear Regression and Multiple Regression
Linear Regression and Multiple Regression
CodeEmporium
 2 Logistic Regression - THE MATH YOU SHOULD KNOW!
Logistic Regression - THE MATH YOU SHOULD KNOW!
CodeEmporium
 3 Generative Adversarial Networks - FUTURISTIC & FUN AI !
Generative Adversarial Networks - FUTURISTIC & FUN AI !
CodeEmporium
 4 Deep Learning on the Cloud - GPU TO LEARN FASTER
Deep Learning on the Cloud - GPU TO LEARN FASTER
CodeEmporium
 5 Deep Mind's AlphaGo Zero - EXPLAINED
Deep Mind's AlphaGo Zero - EXPLAINED
CodeEmporium
 6 Mask Region based Convolution Neural Networks - EXPLAINED!
Mask Region based Convolution Neural Networks - EXPLAINED!
CodeEmporium
 7 Attention in Neural Networks
Attention in Neural Networks
CodeEmporium
 8 Depthwise Separable Convolution - A FASTER CONVOLUTION!
Depthwise Separable Convolution - A FASTER CONVOLUTION!
CodeEmporium
 9 One Neural network learns EVERYTHING ?!
One Neural network learns EVERYTHING ?!
CodeEmporium
 10 Neural Voice Cloning
Neural Voice Cloning
CodeEmporium
 11 AI creates Image Classifiers…by DRAWING?
AI creates Image Classifiers…by DRAWING?
CodeEmporium
 12 Unpaired Image-Image Translation using CycleGANs
Unpaired Image-Image Translation using CycleGANs
CodeEmporium
 13 K-Means Clustering - EXPLAINED!
K-Means Clustering - EXPLAINED!
CodeEmporium
 14 Random Forest Classification
Random Forest Classification
CodeEmporium
 15 Data Science in Finance
Data Science in Finance
CodeEmporium
 16 Hypothesis testing with Applications in Data Science
Hypothesis testing with Applications in Data Science
CodeEmporium
 17 A/B Testing - Simply Explained
A/B Testing - Simply Explained
CodeEmporium
 18 The Kernel Trick - THE MATH YOU SHOULD KNOW!
The Kernel Trick - THE MATH YOU SHOULD KNOW!
CodeEmporium
 19 Support Vector Machines - THE MATH YOU SHOULD KNOW
Support Vector Machines - THE MATH YOU SHOULD KNOW
CodeEmporium
 20 Principal Component Analysis (PCA) - THE MATH YOU SHOULD KNOW!
Principal Component Analysis (PCA) - THE MATH YOU SHOULD KNOW!
CodeEmporium
 21 History of Calculus - Animated
History of Calculus - Animated
CodeEmporium
 22 Curiosity in AI
Curiosity in AI
CodeEmporium
 23 DropBlock - A BETTER DROPOUT for Neural Networks
DropBlock - A BETTER DROPOUT for Neural Networks
CodeEmporium
 24 Autoencoders - EXPLAINED
Autoencoders - EXPLAINED
CodeEmporium
 25 Recurrent Neural Networks - EXPLAINED!
Recurrent Neural Networks - EXPLAINED!
CodeEmporium
 26 LSTM Networks - EXPLAINED!
LSTM Networks - EXPLAINED!
CodeEmporium
 27 Building an Image Captioner with Neural Networks
Building an Image Captioner with Neural Networks
CodeEmporium
 28 10 Machine Learning Questions - ANSWERED!
10 Machine Learning Questions - ANSWERED!
CodeEmporium
 29 How do neural networks work?
How do neural networks work?
CodeEmporium
 30 Evolution of Face Generation | Evolution of GANs
Evolution of Face Generation | Evolution of GANs
CodeEmporium
 31 How does Google Translate's AI work?
How does Google Translate's AI work?
CodeEmporium
 32 How to keep up with AI research?
How to keep up with AI research?
CodeEmporium
 33 How does YouTube recommend videos? - AI EXPLAINED!
How does YouTube recommend videos? - AI EXPLAINED!
CodeEmporium
 34 Variational Autoencoders - EXPLAINED!
Variational Autoencoders - EXPLAINED!
CodeEmporium
 35 Logistic Regression - VISUALIZED!
Logistic Regression - VISUALIZED!
CodeEmporium
 36 Gradient Descent - THE MATH YOU SHOULD KNOW
Gradient Descent - THE MATH YOU SHOULD KNOW
CodeEmporium
 37 Boosting - EXPLAINED!
Boosting - EXPLAINED!
CodeEmporium
 38 Transformer Neural Networks - EXPLAINED! (Attention is all you need)
Transformer Neural Networks - EXPLAINED! (Attention is all you need)
CodeEmporium
 39 Loss Functions - EXPLAINED!
Loss Functions - EXPLAINED!
CodeEmporium
 40 Optimizers - EXPLAINED!
Optimizers - EXPLAINED!
CodeEmporium
 41 NLP with Neural Networks & Transformers
NLP with Neural Networks & Transformers
CodeEmporium
 42 Batch Normalization - EXPLAINED!
Batch Normalization - EXPLAINED!
CodeEmporium
 43 Activation Functions - EXPLAINED!
Activation Functions - EXPLAINED!
CodeEmporium
 44 Data Scientist Answers Interview Questions
Data Scientist Answers Interview Questions
CodeEmporium
 45 Why use GPU with Neural Networks?
Why use GPU with Neural Networks?
CodeEmporium
 46 How do GPUs speed up Neural Network training?
How do GPUs speed up Neural Network training?
CodeEmporium
 47 BERT Neural Network - EXPLAINED!
BERT Neural Network - EXPLAINED!
CodeEmporium
 48 ConvNets Scaled Efficiently
ConvNets Scaled Efficiently
CodeEmporium
 49 Transformer Neural Net makes music! (JukeboxAI)
Transformer Neural Net makes music! (JukeboxAI)
CodeEmporium
 ▶ What do filters of Convolution Neural Network learn?
What do filters of Convolution Neural Network learn?
CodeEmporium
 51 We're hosting a Machine Learning Conference!
We're hosting a Machine Learning Conference!
CodeEmporium
 52 MLconfEU 2020: Machine Learning Conference for Software Engineers
MLconfEU 2020: Machine Learning Conference for Software Engineers
CodeEmporium
 53 Are Neural Networks Intelligent?
Are Neural Networks Intelligent?
CodeEmporium
 54 Time Series Forecasting with Machine Learning
Time Series Forecasting with Machine Learning
CodeEmporium
 55 Few Shot Learning - EXPLAINED!
Few Shot Learning - EXPLAINED!
CodeEmporium
 56 How does a Data Scientist Fight FRAUD?
How does a Data Scientist Fight FRAUD?
CodeEmporium
 57 How would a Data Scientist analyze Customer Churn?
How would a Data Scientist analyze Customer Churn?
CodeEmporium
 58 Expectations with Machine Learning
Expectations with Machine Learning
CodeEmporium
 59 Why Logistic Regression DOESN'T return probabilities?!
Why Logistic Regression DOESN'T return probabilities?!
CodeEmporium
 60 How you SHOULD code Machine Learning
How you SHOULD code Machine Learning
CodeEmporium

This video teaches viewers about the inner workings of Convolution Neural Network filters, including how they learn to classify images and detect objects, with a focus on human interpretability and referencing relevant research papers.

Key Takeaways
  1. Understand how humans classify images
  2. Learn how CNNs classify images
  3. Study bilinear interpolation and its applications
  4. Explore activation functions and their role in CNNs
  5. Investigate Intersection over Union (IoU) and its use in object detection
  6. Read and understand research papers on CNN filters and object detection
💡 CNN filters can learn to detect objects and classify images in a way that is not always human-interpretable, but understanding how they work can improve their performance and applicability.

Related Reads

📰
Understanding Deep Learning Through Four Interactive Experiments
Explore deep learning concepts through interactive experiments to gain hands-on understanding
Medium · Data Science
 📰
Understanding Deep Learning Through Four Interactive Experiments
Explore deep learning through interactive experiments to gain hands-on understanding
Medium · Deep Learning
 📰
Optimizers in Deep Learning: From Gradient Descent to Adam
Learn how optimizers in deep learning work, from basic Gradient Descent to advanced Adam optimizer, to improve model training
Medium · Deep Learning
 📰
The Meta-Architecture of Interface Fracture: High-Dimensional Logical Stress and Systemic Collapse…
Learn about the meta-architecture of interface fracture and its relation to high-dimensional logical stress and systemic collapse in deep learning systems
Medium · Deep Learning

Chapters (8)

Personal Note
0:29 Introduction
2:50 Pass 1: How do Humans classify Images?
3:57 Pass 2: How do networks classify Images?
6:55 Bilinear Interpolation
9:00 Activation Function (the mask)
10:25 Intersection over Union (IoU)
11:20 Interesting findings from main paper
Up next
Image Classification with ml5.js
The Coding Train
Watch →