Architecture search concepts in Computer Vision - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

What is the main goal of Neural Architecture Search (NAS)?

Neural Architecture Search (NAS) is a technique used in machine learning. What is its main goal?

A To increase the size of the training dataset by data augmentation

B To manually tune hyperparameters like learning rate and batch size

C To automatically find the best neural network design for a specific task

D To reduce the number of layers in a neural network to speed up training

💡 Hint

Think about what NAS tries to automate in model building.

NAS automates the process of designing neural network architectures to find the best structure for a given task, instead of manual trial and error.

❓ Model Choice

intermediate

Which architecture search method uses reinforcement learning?

Among the following architecture search methods, which one typically uses reinforcement learning to guide the search?

A Evolutionary algorithms

B Controller RNN-based search

C Random search

D Grid search

💡 Hint

Look for the method that uses a neural network to propose architectures.

Controller RNN-based NAS uses reinforcement learning where the controller RNN proposes architectures and learns from their performance.

❓ Hyperparameter

advanced

In architecture search, what does the 'search space' define?

When performing architecture search, what does the 'search space' specify?

A The range of possible neural network architectures to explore

B The hardware resources available for training

C The learning rate schedule during training

D The dataset used for training the model

💡 Hint

Think about what the search algorithm is choosing from.

The search space defines all possible architectures that the search method can consider during the search process.

❓ Metrics

advanced

Which metric is commonly used to evaluate candidate architectures during NAS?

During Neural Architecture Search, which metric is most commonly used to evaluate how good a candidate architecture is?

A Time taken to train the model

B Training loss on the full training set

C Number of parameters in the model

D Validation accuracy on a held-out dataset

💡 Hint

Think about what shows how well the model generalizes.

Validation accuracy measures how well the architecture performs on unseen data, which is key to selecting good models.

🔧 Debug

expert

What error occurs if the search space is too large without constraints?

In Neural Architecture Search, if the search space is extremely large and unconstrained, what is the most likely problem that will occur?

A The search will take too long and may never find a good architecture

B The model will overfit the training data immediately

C The training will fail due to incompatible layer sizes

D The search will produce architectures with zero parameters

💡 Hint

Consider the impact of a huge number of options to explore.

A very large search space makes the search computationally expensive and slow, often preventing finding good architectures in reasonable time.