Inception Network Design

Requirements Clarification & Assessment

Objective: Understand the application of "Inception Network Design" in convolutional neural networks (CNNs) and identify the challenges in image recognition it addresses.

Clarifications:

• Inception Network Design: Often referred to as the "Inception Architecture" or "GoogLeNet," introduced in a 2015 paper, it is a design pattern in CNNs.
• Challenges in Image Recognition: Primarily involve efficient feature extraction, parameter optimization, computational efficiency, and generalization capabilities.

Assessment:

• Depth vs. Width Trade-off: Need to balance the complexity of feature extraction with computational resources.
• Local vs. Global Feature Capture: Ability to recognize features across different scales and orientations.
• Parameter Efficiency: Reducing the number of parameters without compromising performance.
• Generalization: Ensuring the model's ability to perform well on unseen data.