What is perceptual loss and why is it better than pixel-wise for image quality?
mediumAnswer
- Loss computed as MSE (or cosine similarity) between features from a pretrained network (e.g., VGG-19 conv layers) applied to prediction and target, rather than on raw pixels.
- Pretrained features capture perceptual similarity — small changes in features correspond to visually similar images.
- Pixel-MSE encourages blurry averages of possible outputs; perceptual loss produces sharper, more realistic reconstructions.
- Used in super-resolution, style transfer, image translation.
Check yourself — multiple choice
- Same as pixel MSE
- MSE on VGG features rather than pixels — matches perceptual similarity, avoids blur in generation
- Only for text
- Perceptual loss is deprecated
Perceptual loss = feature-space MSE (VGG features) → sharper, more realistic images.
#losses#generative#computer-vision
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