CycleGAN with Better Cycles (2408.15374v2)

Abstract: CycleGAN provides a framework to train image-to-image translation with unpaired datasets using cycle consistency loss [4]. While results are great in many applications, the pixel level cycle consistency can potentially be problematic and causes unrealistic images in certain cases. In this project, we propose three simple modifications to cycle consistency, and show that such an approach achieves better results with fewer artifacts.

• The paper introduces feature-level cycle consistency to reduce pixel artifacts while preserving semantic content.
• It proposes a gradual decay of cycle consistency weight, enabling more natural and unconstrained image transformations.
• Experimental results on the horse2zebra dataset show improved image realism, paving the way for future GAN architecture research.

Analysis of "CycleGAN with Better Cycles"

The paper "CycleGAN with Better Cycles" by Tongzhou Wang and Yihan Lin from the University of California, Berkeley, addresses significant challenges in the CycleGAN framework for image-to-image translation. The focus is on refining the cycle consistency loss, a critical component that enables training with unpaired datasets. Though CycleGAN has demonstrated success in diverse applications, the authors identify that cycle consistency at the pixel level can lead to unrealistic image artifacts. This paper proposes three modifications aimed at improving the quality of generated images.

CycleGAN Architecture and Cycle Consistency

CycleGAN employs a dual-generator, dual-discriminator architecture to translate images between two domains without paired datasets. Cycle consistency enforces that a translation of an image from domain X to Y and back to X should yield the original image. This concept is crucial in guiding the generators to maintain structural similarities with the input images. However, pixel-level cycle consistency assumes a perfect one-to-one mapping between domains, which is not always feasible and results in artifacts, such as texture patterns or color encodings not intended for the target domain.

Proposed Modifications

The paper presents three key amendments to the cycle consistency mechanism:

1. Feature-Level Cycle Consistency: The authors suggest incorporating CNN feature-level consistency in addition to pixel-level consistency. By using discriminator features to measure image similarity, this modification allows a more relaxed interpretation of cycle consistency, preserving general structures without enforcing a strict pixel match. This approach draws inspiration from perceptual similarity metrics, indicating its potential effectiveness in maintaining semantic coherence.
2. Cycle Consistency Weight Decay: Recognizing the stabilizing impact of cycle consistency at the beginning of training but its hindrances in later stages, the authors propose a gradual decay of cycle consistency weight. This adjustment aims to liberate generators from unnecessary constraints as training progresses, thus facilitating more realistic transformations.
3. Quality-Weighted Cycle Consistency: This method involves weighting cycle consistency loss based on the discriminators' evaluation of image quality. The motivation here is that early in training, generated images may be unrealistic, so enforcing cycle consistency too rigidly could impede learning. By adapting the consistency loss based on image realism, the training process dynamically balances cycle consistency and GAN objectives.

Experimental Results and Observations

Experiments conducted on the horse2zebra dataset exhibit substantial improvements with the proposed modifications. The resulting images showcase reduced artifacts and enhanced realism compared to the original CycleGAN outputs. However, the paper notes that the quality-weighted cycle consistency did not significantly impact results, likely due to the simultaneous training of discriminators and generators, which may lead to non-optimal evaluations. The authors recommend exploring pretrained discriminators for further validation of this approach.

Implications and Future Directions

The modifications suggested in this paper present significant advancements for the CycleGAN architecture by enhancing image realism while addressing specific limitations in the cycle consistency paradigm. This work opens several avenues for future research, such as the exploration of stochastic input for handling one-to-many mappings, the integration of pretrained discriminators, and the potential of designing architectures that consider a shared latent space for image domains. Furthermore, parameter optimization remains a critical area requiring further exploration to harness the full capabilities of the proposed enhancements.

In conclusion, this paper contributes meaningful insights into improving the CycleGAN framework by reformulating cycle consistency constraints. It provides a foundation for subsequent research to further refine image-to-image translation tasks, essential for advancing applications ranging from style transfer to domain adaptation in autonomous systems.