[STUDY GUIDE] • HIGH-YIELD PRACTICE & REVIEW EDITION
CS7643 Quiz 2 Questions & Answer Key
Solutions Latest 2026/2027 Edition
Comprehensive Examination Question Bank • Concept Mapping
TOTAL QUESTIONS EXAM TOPICS ANSWERS
55 Questions 11 Modules 100% Verified
DOCUMENT OVERVIEW
This document contains 55 verified questions with correct answers and detailed explanations related to convolutional neural
networks and image processing techniques. It serves as an essential resource for understanding advanced concepts in computer
vision, making it suitable for study, review, and certification preparation in deep learning and related fields.
EXAM BLUEPRINT & TOPIC DISTRIBUTION
Systematic breakdown of subject domains and exam coverage.
Topic Module Scope & Core Focus Questions Share (%)
Explores the principles of convolution, feature extraction, and the
Convolution and Feature Extraction significance of kernels in image processing. 6 Qs 10.9%
Covers various methods used in image processing, including
Image Processing Techniques pooling, padding, and strides. 5 Qs 9.1%
Discusses different convolutional neural network architectures and
CNN Architectures their key aspects. 5 Qs 9.1%
Examines methodologies and architectures used for object detection
Object Detection Methods in images. 5 Qs 9.1%
Focuses on CNN architectures specifically designed for image
Segmentation Techniques segmentation tasks. 5 Qs 9.1%
Analyzes different types of errors encountered in model training and
Learning and Optimization Errors optimization. 5 Qs 9.1%
Explores the concept and effectiveness of transfer learning in deep
Transfer Learning learning applications. 5 Qs 9.1%
Investigates the impact of adversarial images on neural networks
Adversarial Examples and strategies for robustness. 5 Qs 9.1%
Confidential • Student Study Edition • Practice & Review Guide Page 1 of 13
, STUDENT STUDY & MASTERY EDITION PRACTICE & REVIEW GUIDE
Examines the characteristics of image features and their impact on
Image Features and Properties CNN operations. 5 Qs 9.1%
Covers key evaluation metrics used to assess model performance in
Evaluation Metrics object detection. 5 Qs 9.1%
CNN Gradients and Focuses on the relationship between cross-correlation and gradient
Backpropagation calculations in CNNs. 4 Qs 7.3%
Total Exam Coverage 11 Integrated Topic Modules 55 Qs 100.0%
TOPIC 1: CONVOLUTION AND FEATURE EXTRACTION
6 Questions • 10.9% of Exam • Explores the principles of convolution, feature extraction, and the significance of kernels in image processing.
QUESTION 1
Convolution Features
Answer: Edges colors textures motifs (corners, shapes)
QUESTION 2
Convolution vs Cross-correlation
Answer: Convolution: flip the kernel (rotate 180) and take the dot product with image patch Cross-correlation: do not
flip the kernel to take the dot product with image patch.
QUESTION 3
Advantage of using image patch
1./ Reduces the input parameters to
K1 x K2 + 1 (bias) for each output node. Thus, the total number of input parameters:
Answer: N x (K1 + K2 + 1) 2./ Explicitly maintains spatial information.
Confidential • Student Study Edition • Practice & Review Guide Page 2 of 13
CS7643 Quiz 2 Questions & Answer Key
Solutions Latest 2026/2027 Edition
Comprehensive Examination Question Bank • Concept Mapping
TOTAL QUESTIONS EXAM TOPICS ANSWERS
55 Questions 11 Modules 100% Verified
DOCUMENT OVERVIEW
This document contains 55 verified questions with correct answers and detailed explanations related to convolutional neural
networks and image processing techniques. It serves as an essential resource for understanding advanced concepts in computer
vision, making it suitable for study, review, and certification preparation in deep learning and related fields.
EXAM BLUEPRINT & TOPIC DISTRIBUTION
Systematic breakdown of subject domains and exam coverage.
Topic Module Scope & Core Focus Questions Share (%)
Explores the principles of convolution, feature extraction, and the
Convolution and Feature Extraction significance of kernels in image processing. 6 Qs 10.9%
Covers various methods used in image processing, including
Image Processing Techniques pooling, padding, and strides. 5 Qs 9.1%
Discusses different convolutional neural network architectures and
CNN Architectures their key aspects. 5 Qs 9.1%
Examines methodologies and architectures used for object detection
Object Detection Methods in images. 5 Qs 9.1%
Focuses on CNN architectures specifically designed for image
Segmentation Techniques segmentation tasks. 5 Qs 9.1%
Analyzes different types of errors encountered in model training and
Learning and Optimization Errors optimization. 5 Qs 9.1%
Explores the concept and effectiveness of transfer learning in deep
Transfer Learning learning applications. 5 Qs 9.1%
Investigates the impact of adversarial images on neural networks
Adversarial Examples and strategies for robustness. 5 Qs 9.1%
Confidential • Student Study Edition • Practice & Review Guide Page 1 of 13
, STUDENT STUDY & MASTERY EDITION PRACTICE & REVIEW GUIDE
Examines the characteristics of image features and their impact on
Image Features and Properties CNN operations. 5 Qs 9.1%
Covers key evaluation metrics used to assess model performance in
Evaluation Metrics object detection. 5 Qs 9.1%
CNN Gradients and Focuses on the relationship between cross-correlation and gradient
Backpropagation calculations in CNNs. 4 Qs 7.3%
Total Exam Coverage 11 Integrated Topic Modules 55 Qs 100.0%
TOPIC 1: CONVOLUTION AND FEATURE EXTRACTION
6 Questions • 10.9% of Exam • Explores the principles of convolution, feature extraction, and the significance of kernels in image processing.
QUESTION 1
Convolution Features
Answer: Edges colors textures motifs (corners, shapes)
QUESTION 2
Convolution vs Cross-correlation
Answer: Convolution: flip the kernel (rotate 180) and take the dot product with image patch Cross-correlation: do not
flip the kernel to take the dot product with image patch.
QUESTION 3
Advantage of using image patch
1./ Reduces the input parameters to
K1 x K2 + 1 (bias) for each output node. Thus, the total number of input parameters:
Answer: N x (K1 + K2 + 1) 2./ Explicitly maintains spatial information.
Confidential • Student Study Edition • Practice & Review Guide Page 2 of 13
