Explain dataset size, generalization, and U-Net skips

You are interviewing for an ML Engineer role in an image/video team. Answer the following conceptual questions clearly and concisely. ## 1) Small vs. large datasets - What are the typical advantages/disadvantages of training on a **small** dataset vs a **large** dataset? - How do compute, labeling cost, noise, and distribution shift change your strategy? ## 2) Overfitting vs. underfitting - Define **overfitting** and **underfitting**. - Give practical signs you would observe in training/validation curves. - Provide common fixes for each. ## 3) How do you approach a new modeling task? Describe your end-to-end approach when developing a model for a new task: - Do you start with a small model and small dataset? Why or why not? - What baselines do you set? - How do you iterate (data, model, loss, evaluation)? ## 4) Super-resolution losses and blurriness In single-image super-resolution: - Compare optimizing **pixel-wise MSE (L2)** loss vs a **perceptual loss** (e.g., feature-space loss using a pretrained network). - Which objective more commonly produces **blurry** images, and why? ## 5) U-Net architecture - Explain the U-Net architecture at a high level (encoder/decoder, downsampling/upsampling, skip connections). - What is the purpose of the **skip connections**? - What typically happens if you **remove** skip connections in a U-Net used for dense prediction (segmentation), in terms of optimization and output quality?