1 INTRODUCTION

2 BACKGROUND & MOTIVATION
2.1 Invariance
2.2 Equivariance

3 CAPSULE NETWORK FOUNDATIONS
3.1 The Picasso Problem
3.2 Sub-sampling & Convolution
3.3 Capsule Networks
3.4 What is Capsule Routing?

4 CAPSULE ROUTING MECHANISMS
4.1 Transforming Autoencoders
4.2 Dynamic Routing Between Capsules
4.3 Matrix Capsules with EM-Routing
4.4 Capsule Routing via Variational Bayes
4.5 Uncertainty in Capsule Routing
4.6 Stacked Capsule Autoencoders
4.7 Inference for Generative Capsule Models
4.8 Self-Routing Capsule Networks
4.9 Straight-Through Attentive Routing
4.10 Inverted Dot-Product Attention Routing

5 ATTENTION & CAPSULES
5.1 On Self-Attention & Capsule Routing
5.2 Comparing Inductive Biases
5.3 Slots & Attention
5.4 Transformers for Routing & Sets
5.5 Relational Neural Expectation Maximization

6 CAPSULES FOR VIDEO AND MOTION

7 GEOMETRIC AND GRAPH BASED CAPSULES

8 GENERATIVE ADVERSARIAL CAPSULE NETWORKS

9 CAPSULES FOR NATURAL LANGUAGE PROCESSING

10 CAPSULES FOR MEDICAL IMAGE ANALYSIS

11 CAPSULES IN OTHER APPLICATIONS
11.1 Fault Diagnosis
11.2 Hyperspectral Images
11.3 Forgery Detection
11.4 Adversarial Attacks

12 DISCUSSION & FUTURE DIRECTIONS
12.1 The Hardware Lottery
12.2 The Binding Problem
12.3 Limitations & Open Challenges