Quiz on Computer Vision Model Compression Techniques

What is the main focus of the paper?

• Web Design

• Image Editing Techniques

• Game Development

• Model Compression Techniques

What does Knowledge Distillation aim to do?

• Make models bigger

• Reduce the number of layers

• Transfer knowledge from a teacher model to a student model

• Increase the number of parameters

Which technique reduces the size of a neural network by removing unnecessary parts?

• Network Pruning

• Image Resizing

• Data Augmentation

• Feature Extraction

What is the purpose of Network Quantization?

• To improve image quality

• To increase the number of colors in images

• To add more layers to a model

• To convert network parameters to lower precision

What is a challenge of using Low-Rank Matrix Factorization?

• Finding the right rank for factorization

• Reducing data size

• Making models larger

• Increasing training time

What is one benefit of using model compression techniques?

• They require more power

• They increase the number of parameters

• They help models run on devices with limited resources

• They make models slower

Which of the following is NOT a model compression technique mentioned?

• Knowledge Distillation

• Network Quantization

• Network Pruning

• Image Filtering

What is the expected outcome of using compression techniques?

• More complex models

• Increased internet speed

• Faster execution time and lower memory consumption

• Higher memory usage

What does KD stand for in model compression?

• Knowledge Distillation

• Kernel Density

• Kinetic Dynamics

• Knowledge Development

Which model is mentioned as having a 56% accuracy drop?

• DenseNet-BC-100

• VGG-19

• ResNet50

• MobileNetV2