LaMa Resolution-Robust Inpainting Quiz

Answers and explanations appear as you take the quiz.

1. What is the signature architectural component that gives LaMa its global view of an image?
   • Fast Fourier Convolutions
   • Transformer self-attention blocks
   • Recurrent LSTM layers
   • Capsule networks
2. Why does LaMa handle large masks better than many earlier inpainting models?
   • It only works on tiny images
   • Its global receptive field lets it continue structure across big holes
   • It copies pixels randomly from the edges
   • It refuses to fill masks above a fixed size
3. What resolution behavior did the LaMa paper report?
   • It only works at one exact training resolution
   • It works only below the training resolution
   • It generalized to some resolutions higher than those used during training in the evaluated settings
   • It accepts no image input
4. What kind of textures does LaMa handle especially well thanks to its frequency-domain processing?
   • Pure random noise
   • Only solid single colors
   • Text characters exclusively
   • Repetitive, periodic textures like brick or tile
5. Compared with large diffusion-based inpainting systems, how is LaMa commonly positioned?
   • As a comparatively efficient convolutional inpainting method
   • As a much slower model that requires a data center
   • As a video-only model
   • As a tool that never needs an image
6. Which loss helps LaMa produce realistic-looking fills rather than blurry averages?
   • A simple pixel mean-squared-error loss alone
   • An adversarial loss combined with a perceptual loss
   • No loss function at all
   • A loss that only measures file size
7. What additional loss component does the LaMa paper emphasize for large-mask inpainting?
   • A speech-recognition loss
   • A bitrate-only loss
   • A perceptual loss with a high receptive field
   • A loss that ignores image structure
8. Who introduced LaMa in the 2021 paper?
   • Jonathan Frankle and Michael Carbin
   • Cees Taal and colleagues
   • Timothy Dozat and Christopher Manning
   • Roman Suvorov and colleagues