Unsupervised Image-To-Image Translation

Cycle Consistency Loss is a type of loss used for generative adversarial networks that performs unpaired image-to-image translation. It was introduced with the CycleGAN architecture. For two domains $X$ and $Y$, we want to learn a mapping $G : X \rightarrow Y$ and $F: Y \rightarrow X$. We want to enforce the intuition that these mappings should be reverses of each other and that both mappings should be bijections. Cycle Consistency Loss encourages $F\left(G\left(x\right)\right) \approx x$ and $G\left(F\left(y\right)\right) \approx y$. It reduces the space of possible mapping functions by enforcing forward and backwards consistency: $$ \mathcal{L}\_{cyc}\left(G, F\right) = \mathbb{E}\_{x \sim p\_{data}\left(x\right)}\left[||F\left(G\left(x\right)\right) - x||\_{1}\right] + \mathbb{E}\_{y \sim p\_{data}\left(y\right)}\left[||G\left(F\left(y\right)\right) - y||\_{1}\right] $$

COCO-FUNIT is few-shot image translation model which computes the style embedding of the example images conditioned on the input image and a new module called the constant style bias. It builds on top of FUNIT by identifying the content loss problem and then addressing it with a novel content-conditioned style encoder architecture. The FUNIT method suffers from the content loss problem—the translation result is not well-aligned with the input image. While a direct theoretical analysis is likely elusive, we conduct an empirical study, aiming at identify the cause of the content loss problem. In analyses, the authors show that the FUNIT style encoder produces very different style codes using different crops -- suggesting the style code contains other information about the style image such as the object pose. To make the style embedding more robust to small variations in the style image, a new style encoder architecture, the Content-Conditioned style encoder (COCO), is introduced. The most distinctive feature of this new encoder is the conditioning in the content image as illustrated in the top-right of the Figure. Unlike the style encoder in FUNIT, COCO takes both content and style image as input. With this content-conditioning scheme, a direct feedback path is created during learning to let the content image influence how the style code is computed. It also helps reduce the direct influence of the style image to the extract style code.

The Content-Conditioned Style Encoder, or COCO, is a style encoder used for image-to-image translation in the COCO-FUNIT architecture. Unlike the style encoder in FUNIT, COCO takes both content and style image as input. With this content conditioning scheme, we create a direct feedback path during learning to let the content image influence how the style code is computed. It also helps reduce the direct influence of the style image to the extract style code. The bottom part of the Figure details architecture. First, the content image is fed into an encoder $E\_{S, C}$ to compute a spatial feature map. This content feature map is then mean-pooled and mapped to a vector $\zeta\_{c} .$ Similarly, the style image is fed into encoder $E\_{S, S}$ to compute a spatial feature map. The style feature map is then mean-pooled and concatenated with an input-independent bias vector: the constant style bias (CSB). Note that while the regular bias in deep networks is added to the activations, in CSB, the bias is concatenated with the activations. The CSB provides a fixed input to the style encoder, which helps compute a style code that is less sensitive to the variations in the style image. The concatenation of the style vector and the CSB is mapped to a vector $\zeta\_{s}$ via a fully connected layer. We then perform an element-wise product operation to $\zeta\_{c}$ and $\zeta\_{s}$, which is the final style code. The style code is then mapped to produce the AdaIN parameters for generating the translation. Through this element-wise product operation, the resulting style code is heavily influenced by the content image. One way to look at this mechanism is that it produces a customized style code for the input content image. The COCO is used as a drop-in replacement for the style encoder in FUNIT. Let $\phi$ denote the COCO mapping. The translation output is then computed via $$ z\_{c}=E\_{c}\left(x_{c}\right), z_{s}=\phi\left(E\_{s, s}\left(x_{s}\right), E\_{s, c}\left(x\_{c}\right)\right), \overline{\mathbf{x}}=F\left(z\_{c}, z\_{s}\right) $$ The style code extracted by the COCO is more robust to variations in the style image. Note that we set $E\_{S, C} \equiv E\_{C}$ to keep the number of parameters in our model similar to that in FUNIT.

DeepIR, or Deep InfraRed image processing, is a thermal image processing framework for recovering high quality images from a very small set of images captured with camera motion. Enhancement is achieved by noting that camera motion, which is usually a hinderance, can be exploited to our advantage to separate a sequence of images into the scene-dependent radiant flux, and a slowly changing scene-independent non-uniformity. DeepIR combines the physics of microbolometer sensors, with powerful regularization capabilities by neural network-based representations. DeepIR relies on the key observation that jittering a camera, while unwanted in visible domain, is highly desirable in the thermal domain as it allows an accurate separation of the sensor-specific non-uniformities from the scene’s radiant flux.