Architecture search concepts in Computer Vision - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is Neural Architecture Search (NAS)?

Neural Architecture Search is a method to automatically find the best design of a neural network for a specific task, instead of manually choosing the layers and connections.

beginner

Why is architecture search important in computer vision?

Because the right network design can improve accuracy and speed for tasks like image recognition, making models better at understanding pictures.

intermediate

What are the three main components of architecture search?

1. Search space: all possible network designs to explore. 2. Search strategy: how to explore these designs. 3. Performance estimation: how to quickly check if a design works well.

beginner

What is a search space in architecture search?

The search space is the set of all possible network structures that the search method can try, like different numbers of layers, types of layers, or connections.

intermediate

Name two common search strategies used in architecture search.

Two common strategies are: 1) Reinforcement Learning, where a controller learns to pick good designs, and 2) Evolutionary Algorithms, which mimic natural selection to improve designs over time.

What does the 'performance estimation' component do in architecture search?

A Measures how well a network design works quickly

B Defines all possible network designs

C Chooses which designs to try next

D Trains the final model on all data

Performance estimation quickly checks if a network design performs well, so the search can focus on promising designs.

Which of these is NOT typically part of the search space in architecture search?

A Connections between layers

B Type of activation functions

C Number of layers

D Color of the computer case

The color of the computer case is unrelated to neural network design and not part of the search space.

What is a benefit of using Neural Architecture Search?

A It guarantees 100% accuracy

B It makes networks run on any device without changes

C It removes the need for manual design of networks

D It replaces the need for training data

NAS automates the design process, reducing manual trial and error.

Which search strategy uses a controller to learn good network designs?

A Evolutionary Algorithms

B Reinforcement Learning

C Grid Search

D Random Search

Reinforcement Learning uses a controller that learns to pick better designs over time.

In architecture search, what does the term 'search strategy' mean?

A The way to explore different network designs

B The final model's accuracy

C The hardware used for training

D The dataset used for testing

Search strategy defines how the search process tries different network designs.

Explain the three main components of architecture search and why each is important.

Think about what you need to explore, how you explore, and how you check results.

Describe how Neural Architecture Search can improve computer vision models compared to manual design.

Consider the benefits of letting a computer find the best design.