Interview Practice Questions

Explain what data augmentation is and what role it plays in training computer vision models.

What is IoU and how is it used to evaluate the performance of object detection models?

Compare the VGG and ResNet architectures in terms of their approaches to building deep neural networks.

How does the Non-Maximum Suppression (NMS) mechanism work in object detection tasks?

Explain how deep generative models work and provide examples.

What are diffusion models and how do they differ from traditional image generation approaches?

Explain how transfer learning works and in which cases it is advisable to use it.

What are the main issues and challenges associated with training GANs, and how can they be overcome?

What is a convolution and how does it differ from the conventional convolution operation in signal processing?

Explain what pooling is in convolutional neural networks and why it is used.

What is the difference between standard convolution and depthwise separable convolution?

When should data augmentation be used and what are some common methods?

Explain how the attention mechanism works in transformers.

What are the advantages of using GANs over traditional image generation methods?

Explain how Non-Maximum Suppression (NMS) works and what it is used for.

How does transfer learning work and what steps are typically involved in its implementation?

When should quantization and pruning be applied in the context of deploying models on edge devices?

Explain the difference between pointwise convolution and depthwise convolution.

What is mode collapse in the context of GANs and how does it affect training?

How does the attention mechanism work in transformers like ViT?