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Title: PixelNN: Example-based Image Synthesis

Paper content:
We present a simple nearest-neighbor (NN) approach that synthesizes high-frequency photorealistic images from an ``incomplete'' signal such as a low-resolution image, a surface normal map, or edges.
Current state-of-the-art deep generative models designed for such conditional image synthesis lack two important things: (1) they are unable to generate a large set of diverse outputs, due to the mode collapse problem.
(2) they are not interpretable, making it difficult to control the synthesized output.
We demonstrate that NN approaches potentially address such limitations, but suffer in accuracy on small datasets.
We design a simple pipeline that combines the best of both worlds:  the first stage uses a convolutional neural network (CNN) to map the input to a (overly-smoothed) image, and the second stage uses a pixel-wise nearest neighbor method to map the smoothed output to multiple high-quality, high-frequency outputs in a controllable manner.
Importantly, pixel-wise matching allows our method to compose novel high-frequency content by cutting-and-pasting pixels from different training exemplars.
We demonstrate our approach for various input modalities, and for various domains ranging from human faces, pets, shoes, and handbags.
We consider the task of generating high-resolution photo-realistic images from incomplete input such as a low-resolution image, sketches, surface normal map, or label mask.
Such a task has a number of practical applications such as upsampling/colorizing legacy footage, texture synthesis for graphics applications, and semantic image understanding for vision through analysis-by-synthesis.
These problems share a common underlying structure: a human/machine is given a signal that is missing considerable details, and the task is to reconstruct plausible details.Consider the edge map of cat in Figure 1 -c.
When we humans look at this edge map, we can easily imagine multiple variations of whiskers, eyes, and stripes that could be viable and pleasing to the eye.
Indeed, the task of image synthesis has been well explored, not just for its practical applications but also for its aesthetic appeal.GANs: Current state-of-the-art approaches rely on generative adversarial networks (GANs) BID17 , and most relevant to us, conditional GANS that generate image conditioned on an input signal BID9 BID36 BID23 .
We argue that there are two prominent limitations to such popular formalisms: (1) First and foremost, humans can imagine multiple plausible output images given a incomplete input.
We see this rich space of potential outputs as a vital part of the human capacity to imagine and generate.
Conditional GANs are in principle able to generate multiple outputs through the injection of noise, but in practice suffer from limited diversity (i.e., mode collapse) FIG1 .
Recent approaches even remove the noise altogether, treating conditional image synthesis as regression problem BID5 .
(2) Deep networks are still difficult to explain or interpret, making the synthesized output difficult to modify.
One implication is that users are not able to control the synthesized output.
Moreover, the right mechanism for even specifying user constraints (e.g., "generate a cat image that looks like my cat") is unclear.
This restricts applicability, particularly for graphics tasks.
We present a simple approach to image synthesis based on compositional nearest-neighbors.
Our approach somewhat suggests that GANs themselves may operate in a compositional "copy-andpaste" fashion.
Indeed, examining the impressive outputs of recent synthesis methods suggests that some amount of local memorization is happening.
However, by making this process explicit, our system is able to naturally generate multiple outputs, while being interpretable and amenable to user constraints.
An interesting byproduct of our approach is dense pixel-level correspondences.
If training images are augmented with semantic label masks, these labels can be transfered using our correspondences, implying that our approach may also be useful for image analysis through label transfer BID30 .

assistant: text: Pixel-wise nearest neighbors used for generating multiple images from incomplete priors such as a low-res images, surface normals, edges etc.