Do Vision Transformers See Like Convolutional Neural Networks? | Paper Explained

Aleksa Gordić - The AI Epiphany · Beginner ·👁️ Computer Vision ·4y ago
❤️ Become The AI Epiphany Patreon ❤️ ► https://www.patreon.com/theaiepiphany In this video I cover the "Do Vision Transformers See Like Convolutional Neural Networks?" paper. They dissect ViTs and ResNets and show the differences in the features learned as well as what contributes to those differences (like the amount of data used, skip connections, etc.). ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ ✅ Paper: https://arxiv.org/abs/2108.08810 ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ ⌚️ Timetable: 00:00 Intro 00:45 Contrasting features in ViTs vs CNNs 06:45 Global vs Local receptive fields 13:55 Data matters, mr. obvious 17:40 Contrasting receptive fields 20:30 Data flow through CLS vs spatial tokens 23:30 Skip connections matter a lot in ViTs 24:20 Spatial information is preserved in ViTs 30:10 Features evolution with the amount of data 32:20 Outro ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 💰 BECOME A PATREON OF THE AI EPIPHANY ❤️ If these videos, GitHub projects, and blogs help you, consider helping me out by supporting me on Patreon! The AI Epiphany ► https://www.patreon.com/theaiepiphany One-time donation: https://www.paypal.com/paypalme/theaiepiphany Much love! ❤️ Huge thank you to these AI Epiphany patreons: Eli Mahler Petar Veličković Zvonimir Sabljic ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 💡 The AI Epiphany is a channel dedicated to simplifying the field of AI using creative visualizations and in general, a stronger focus on geometrical and visual intuition, rather than the algebraic and numerical "intuition". ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 👋 CONNECT WITH ME ON SOCIAL LinkedIn ► https://www.linkedin.com/in/aleksagordic/ Twitter ► https://twitter.com/gordic_aleksa Instagram ► https://www.instagram.com/aiepiphany/ Facebook ► https://www.facebook.com/aiepiphany/ 👨‍👩‍👧‍👦 JOIN OUR DISCORD COMMUNITY: Discord ► https://discord.gg/peBrCpheKE 📢 SUBSCRIBE TO MY MONTHLY AI NEWSLETTER: Substack ► https://aiepiphany.substack.com/ 💻 FOLLOW ME ON GITHUB FOR ML PROJECTS: GitHub ► https://github.com/gordicale
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Uploads from Aleksa Gordić - The AI Epiphany · Aleksa Gordić - The AI Epiphany · 59 of 60

1 Intro | Neural Style Transfer #1
Intro | Neural Style Transfer #1
Aleksa Gordić - The AI Epiphany
2 Basic Theory | Neural Style Transfer #2
Basic Theory | Neural Style Transfer #2
Aleksa Gordić - The AI Epiphany
3 Optimization method | Neural Style Transfer #3
Optimization method | Neural Style Transfer #3
Aleksa Gordić - The AI Epiphany
4 Advanced Theory | Neural Style Transfer #4
Advanced Theory | Neural Style Transfer #4
Aleksa Gordić - The AI Epiphany
5 Anyone can make deepfakes now!
Anyone can make deepfakes now!
Aleksa Gordić - The AI Epiphany
6 What is Computer Vision? | The Art of Creating Seeing Machines
What is Computer Vision? | The Art of Creating Seeing Machines
Aleksa Gordić - The AI Epiphany
7 Feed-forward method | Neural Style Transfer #5
Feed-forward method | Neural Style Transfer #5
Aleksa Gordić - The AI Epiphany
8 Alan Turing | Computing Machinery and Intelligence
Alan Turing | Computing Machinery and Intelligence
Aleksa Gordić - The AI Epiphany
9 Feed-forward method (training) | Neural Style Transfer #6
Feed-forward method (training) | Neural Style Transfer #6
Aleksa Gordić - The AI Epiphany
10 What is Google Deep Dream? (Basic Theory) | Deep Dream Series #1
What is Google Deep Dream? (Basic Theory) | Deep Dream Series #1
Aleksa Gordić - The AI Epiphany
11 Semantic Segmentation in PyTorch | Neural Style Transfer #7
Semantic Segmentation in PyTorch | Neural Style Transfer #7
Aleksa Gordić - The AI Epiphany
12 How to get started with Machine Learning
How to get started with Machine Learning
Aleksa Gordić - The AI Epiphany
13 How to learn PyTorch? (3 easy steps) | 2021
How to learn PyTorch? (3 easy steps) | 2021
Aleksa Gordić - The AI Epiphany
14 PyTorch or TensorFlow?
PyTorch or TensorFlow?
Aleksa Gordić - The AI Epiphany
15 3 Machine Learning Projects For Beginners (Highly visual) | 2021
3 Machine Learning Projects For Beginners (Highly visual) | 2021
Aleksa Gordić - The AI Epiphany
16 Machine Learning Projects (Intermediate level) | 2021
Machine Learning Projects (Intermediate level) | 2021
Aleksa Gordić - The AI Epiphany
17 Cheapest (0$) Deep Learning Hardware Options | 2021
Cheapest (0$) Deep Learning Hardware Options | 2021
Aleksa Gordić - The AI Epiphany
18 How to learn deep learning? (Transformers Example)
How to learn deep learning? (Transformers Example)
Aleksa Gordić - The AI Epiphany
19 How do transformers work? (Attention is all you need)
How do transformers work? (Attention is all you need)
Aleksa Gordić - The AI Epiphany
20 Developing a deep learning project (case study on transformer)
Developing a deep learning project (case study on transformer)
Aleksa Gordić - The AI Epiphany
21 Vision Transformer (ViT) - An image is worth 16x16 words | Paper Explained
Vision Transformer (ViT) - An image is worth 16x16 words | Paper Explained
Aleksa Gordić - The AI Epiphany
22 GPT-3 - Language Models are Few-Shot Learners | Paper Explained
GPT-3 - Language Models are Few-Shot Learners | Paper Explained
Aleksa Gordić - The AI Epiphany
23 Google DeepMind's AlphaFold 2 explained! (Protein folding, AlphaFold 1, a glimpse into AlphaFold 2)
Google DeepMind's AlphaFold 2 explained! (Protein folding, AlphaFold 1, a glimpse into AlphaFold 2)
Aleksa Gordić - The AI Epiphany
24 Attention Is All You Need (Transformer) | Paper Explained
Attention Is All You Need (Transformer) | Paper Explained
Aleksa Gordić - The AI Epiphany
25 Graph Attention Networks (GAT) | GNN Paper Explained
Graph Attention Networks (GAT) | GNN Paper Explained
Aleksa Gordić - The AI Epiphany
26 Graph Convolutional Networks (GCN) | GNN Paper Explained
Graph Convolutional Networks (GCN) | GNN Paper Explained
Aleksa Gordić - The AI Epiphany
27 Graph SAGE - Inductive Representation Learning on Large Graphs | GNN Paper Explained
Graph SAGE - Inductive Representation Learning on Large Graphs | GNN Paper Explained
Aleksa Gordić - The AI Epiphany
28 PinSage - Graph Convolutional Neural Networks for Web-Scale Recommender Systems | Paper Explained
PinSage - Graph Convolutional Neural Networks for Web-Scale Recommender Systems | Paper Explained
Aleksa Gordić - The AI Epiphany
29 OpenAI CLIP - Connecting Text and Images | Paper Explained
OpenAI CLIP - Connecting Text and Images | Paper Explained
Aleksa Gordić - The AI Epiphany
30 Temporal Graph Networks (TGN) | GNN Paper Explained
Temporal Graph Networks (TGN) | GNN Paper Explained
Aleksa Gordić - The AI Epiphany
31 Graph Neural Network Project Update! (I'm coding GAT from scratch)
Graph Neural Network Project Update! (I'm coding GAT from scratch)
Aleksa Gordić - The AI Epiphany
32 Graph Attention Network Project Walkthrough
Graph Attention Network Project Walkthrough
Aleksa Gordić - The AI Epiphany
33 How to get started with Graph ML? (Blog walkthrough)
How to get started with Graph ML? (Blog walkthrough)
Aleksa Gordić - The AI Epiphany
34 DQN - Playing Atari with Deep Reinforcement Learning | RL Paper Explained
DQN - Playing Atari with Deep Reinforcement Learning | RL Paper Explained
Aleksa Gordić - The AI Epiphany
35 AlphaGo - Mastering the game of Go with deep neural networks and tree search | RL Paper Explained
AlphaGo - Mastering the game of Go with deep neural networks and tree search | RL Paper Explained
Aleksa Gordić - The AI Epiphany
36 DeepMind's AlphaGo Zero and AlphaZero | RL paper explained
DeepMind's AlphaGo Zero and AlphaZero | RL paper explained
Aleksa Gordić - The AI Epiphany
37 OpenAI - Solving Rubik's Cube with a Robot Hand | RL paper explained
OpenAI - Solving Rubik's Cube with a Robot Hand | RL paper explained
Aleksa Gordić - The AI Epiphany
38 MuZero - Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model | RL Paper explained
MuZero - Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model | RL Paper explained
Aleksa Gordić - The AI Epiphany
39 EfficientNetV2 - Smaller Models and Faster Training | Paper explained
EfficientNetV2 - Smaller Models and Faster Training | Paper explained
Aleksa Gordić - The AI Epiphany
40 Implementing DeepMind's DQN from scratch! | Project Update
Implementing DeepMind's DQN from scratch! | Project Update
Aleksa Gordić - The AI Epiphany
41 MLP-Mixer: An all-MLP Architecture for Vision | Paper explained
MLP-Mixer: An all-MLP Architecture for Vision | Paper explained
Aleksa Gordić - The AI Epiphany
42 DeepMind's Android RL Environment - AndroidEnv
DeepMind's Android RL Environment - AndroidEnv
Aleksa Gordić - The AI Epiphany
43 When Vision Transformers Outperform ResNets without Pretraining | Paper Explained
When Vision Transformers Outperform ResNets without Pretraining | Paper Explained
Aleksa Gordić - The AI Epiphany
44 Non-Parametric Transformers | Paper explained
Non-Parametric Transformers | Paper explained
Aleksa Gordić - The AI Epiphany
45 Chip Placement with Deep Reinforcement Learning | Paper Explained
Chip Placement with Deep Reinforcement Learning | Paper Explained
Aleksa Gordić - The AI Epiphany
46 Text Style Brush - Transfer of text aesthetics from a single example | Paper Explained
Text Style Brush - Transfer of text aesthetics from a single example | Paper Explained
Aleksa Gordić - The AI Epiphany
47 Graphormer - Do Transformers Really Perform Bad for Graph Representation? | Paper Explained
Graphormer - Do Transformers Really Perform Bad for Graph Representation? | Paper Explained
Aleksa Gordić - The AI Epiphany
48 GANs N' Roses: Stable, Controllable, Diverse Image to Image Translation | Paper Explained
GANs N' Roses: Stable, Controllable, Diverse Image to Image Translation | Paper Explained
Aleksa Gordić - The AI Epiphany
49 VQ-VAEs: Neural Discrete Representation Learning | Paper + PyTorch Code Explained
VQ-VAEs: Neural Discrete Representation Learning | Paper + PyTorch Code Explained
Aleksa Gordić - The AI Epiphany
50 VQ-GAN: Taming Transformers for High-Resolution Image Synthesis | Paper Explained
VQ-GAN: Taming Transformers for High-Resolution Image Synthesis | Paper Explained
Aleksa Gordić - The AI Epiphany
51 Multimodal Few-Shot Learning with Frozen Language Models | Paper Explained
Multimodal Few-Shot Learning with Frozen Language Models | Paper Explained
Aleksa Gordić - The AI Epiphany
52 Focal Transformer: Focal Self-attention for Local-Global Interactions in Vision Transformers
Focal Transformer: Focal Self-attention for Local-Global Interactions in Vision Transformers
Aleksa Gordić - The AI Epiphany
53 AudioCLIP: Extending CLIP to Image, Text and Audio | Paper Explained
AudioCLIP: Extending CLIP to Image, Text and Audio | Paper Explained
Aleksa Gordić - The AI Epiphany
54 RMA: Rapid Motor Adaptation for Legged Robots | Paper Explained
RMA: Rapid Motor Adaptation for Legged Robots | Paper Explained
Aleksa Gordić - The AI Epiphany
55 DALL-E: Zero-Shot Text-to-Image Generation | Paper Explained
DALL-E: Zero-Shot Text-to-Image Generation | Paper Explained
Aleksa Gordić - The AI Epiphany
56 DETR: End-to-End Object Detection with Transformers | Paper Explained
DETR: End-to-End Object Detection with Transformers | Paper Explained
Aleksa Gordić - The AI Epiphany
57 DINO: Emerging Properties in Self-Supervised Vision Transformers | Paper Explained!
DINO: Emerging Properties in Self-Supervised Vision Transformers | Paper Explained!
Aleksa Gordić - The AI Epiphany
58 DeepMind DetCon: Efficient Visual Pretraining with Contrastive Detection | Paper Explained
DeepMind DetCon: Efficient Visual Pretraining with Contrastive Detection | Paper Explained
Aleksa Gordić - The AI Epiphany
Do Vision Transformers See Like Convolutional Neural Networks? | Paper Explained
Do Vision Transformers See Like Convolutional Neural Networks? | Paper Explained
Aleksa Gordić - The AI Epiphany
60 Fastformer: Additive Attention Can Be All You Need | Paper Explained
Fastformer: Additive Attention Can Be All You Need | Paper Explained
Aleksa Gordić - The AI Epiphany

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Chapters (10)

Intro
0:45 Contrasting features in ViTs vs CNNs
6:45 Global vs Local receptive fields
13:55 Data matters, mr. obvious
17:40 Contrasting receptive fields
20:30 Data flow through CLS vs spatial tokens
23:30 Skip connections matter a lot in ViTs
24:20 Spatial information is preserved in ViTs
30:10 Features evolution with the amount of data
32:20 Outro
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