Image-to-Image Translation via Group-wise Deep Whitening-and-Coloring Transformation (1812.09912v2)

Abstract: Recently, unsupervised exemplar-based image-to-image translation, conditioned on a given exemplar without the paired data, has accomplished substantial advancements. In order to transfer the information from an exemplar to an input image, existing methods often use a normalization technique, e.g., adaptive instance normalization, that controls the channel-wise statistics of an input activation map at a particular layer, such as the mean and the variance. Meanwhile, style transfer approaches similar task to image translation by nature, demonstrated superior performance by using the higher-order statistics such as covariance among channels in representing a style. In detail, it works via whitening (given a zero-mean input feature, transforming its covariance matrix into the identity). followed by coloring (changing the covariance matrix of the whitened feature to those of the style feature). However, applying this approach in image translation is computationally intensive and error-prone due to the expensive time complexity and its non-trivial backpropagation. In response, this paper proposes an end-to-end approach tailored for image translation that efficiently approximates this transformation with our novel regularization methods. We further extend our approach to a group-wise form for memory and time efficiency as well as image quality. Extensive qualitative and quantitative experiments demonstrate that our proposed method is fast, both in training and inference, and highly effective in reflecting the style of an exemplar. Finally, our code is available at https://github.com/WonwoongCho/GDWCT.

• The paper presents GDWCT, a novel method that approximates the whitening-and-coloring transformation to capture complex style features.
• It outperforms methods like DRIT, MUNIT, and WCT by balancing image quality and speed, as demonstrated in user studies and classification tests.
• The approach offers practical benefits for real-time image editing and lays the groundwork for scalable and multi-modal translation architectures.

Image-to-Image Translation via Group-wise Deep Whitening-and-Coloring Transformation: An Expert Analysis

The paper presents a novel methodology in the domain of image-to-image translation, leveraging Group-wise Deep Whitening-and-Coloring Transformation (GDWCT) to enhance the visual fidelity and style compatibility of translated images. This approach addresses computational inefficiencies and stylistic limitations inherent in existing normalization-based translation methods. By employing this innovative transformation technique, GDWCT aims to deliver superior translation results, reflecting style features with notably higher accuracy and efficiency.

Methodology Overview

The work is rooted in the challenge of unsupervised exemplar-based image translation—specifically, converting image attributes between domains without direct pairing between input and output images. Traditional approaches rely heavily on channel-wise statistics manipulations such as adaptive instance normalization. However, these techniques often fail to encapsulate complex style features effectively, as they primarily adjust only mean and variance without considering more intricate statistical correlations.

GDWCT innovates by modeling the whitening-and-coloring process more comprehensively. First, the paper introduces a method to approximate the computationally demanding whitening-and-coloring transformation used in style transfer. This approximation is achieved with enhanced regularization techniques that allow for an efficient end-to-end training framework. The group-wise formulation further optimizes memory usage and processing time, substantially reducing the complexity from the traditional $O(n^3)$ time complexity.

Results and Performance

The GDWCT model is tested against established methods such as DRIT, MUNIT, and WCT, across diverse datasets including CelebA and Artworks datasets. The empirical results demonstrate GDWCT's capability to produce higher-quality translated images, as indicated by both user studies and quantitative classification accuracy metrics. User preferences heavily favored GDWCT outputs on multiple attributes such as gender transformation and facial expressions, showcasing its enhanced ability to reflect the exemplars' styles robustly.

Furthermore, in terms of classification accuracy, which tests whether translated images maintain identifiable traits of the target domain, GDWCT exhibits competitive performance, marginally trailing DRIT only in specific cases. The efficiency is notable as well—GDWCT achieves a balance between translation quality and processing speed, outperforming slower methods like WCT while matching the speed of other concurrent systems.

Implications and Future Directions

Practically, the GDWCT methodology holds significant implications for real-time image editing applications, where quick style adaptation with high fidelity is requisite. Moreover, by successfully incorporating complex statistical features into translation processes, this approach paves the way for more nuanced, flexible image editing tools that professionals in creative industries can employ for diverse tasks from photo enhancement to automated graphic design.

Theoretically, the work contributes to the broader dialogue on neural style transfer by demonstrating the importance of higher-order statistics in visual representation learning. This method's group-wise handling of statistical transformations might inspire new architectures in neural networks that further optimize the style-content separation or other domain adaptation challenges.

As the field progresses, exploring the scalability of GDWCT for larger datasets or more complex transformations will be vital. Potentially integrating this model with advanced, scalable architectures like transformers could lead to even more powerful translation tools. Furthermore, extending the transformation principles to three-dimensional image data or other modalities could broaden the applicability of this work substantially.

In conclusion, GDWCT is a significant step forward in image translation, demonstrating how theoretical advancements can directly translate into practical improvements for style consistency and computational efficiency. The paper offers a robust framework that other researchers might build upon, ensuring continuous innovation in this rapidly evolving field.