Image-to-Image Translation Framework Embedded with Rotation Symmetry Priors

• URL: http://arxiv.org/abs/2604.12805v1
• Date: Tue, 14 Apr 2026 14:35:42 GMT
• Title: Image-to-Image Translation Framework Embedded with Rotation Symmetry Priors
• Abstract summary: Image-to-image translation (I2I) is a fundamental task in computer vision.<n>We introduce rotation group equivariant convolutions to achieve rotation equivariant I2I framework.<n>We conduct a systematic study on image symmetry priors on real dataset.
• Score: 48.17405932206745
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image-to-image translation (I2I) is a fundamental task in computer vision, focused on mapping an input image from a source domain to a corresponding image in a target domain while preserving domain-invariant features and adapting domain-specific attributes. Despite the remarkable success of deep learning-based I2I approaches, the lack of paired data and unsupervised learning framework still hinder their effectiveness. In this work, we address the challenge by incorporating transformation symmetry priors into image-to-image translation networks. Specifically, we introduce rotation group equivariant convolutions to achieve rotation equivariant I2I framework, a novel contribution, to the best of our knowledge, along this research direction. This design ensures the preservation of rotation symmetry, one of the most intrinsic and domain-invariant properties of natural and scientific images, throughout the network. Furthermore, we conduct a systematic study on image symmetry priors on real dataset and propose a novel transformation learnable equivariant convolutions (TL-Conv) that adaptively learns transformation groups, enhancing symmetry preservation across diverse datasets. We also provide a theoretical analysis of the equivariance error of TL-Conv, proving that it maintains exact equivariance in continuous domains and provide a bound for the error in discrete cases. Through extensive experiments across a range of I2I tasks, we validate the effectiveness and superior performance of our approach, highlighting the potential of equivariant networks in enhancing generation quality and its broad applicability. Our code is available at https://github.com/tanfy929/Equivariant-I2I

Related papers

• Aligning Network Equivariance with Data Symmetry: A Theoretical Framework and Adaptive Approach for Image Restoration [71.51826063983434]
  Image restoration is an inherently ill posed inverse problem.<n>We use non strict symmetry as a constraint to formulate the restoration inverse problem.<n>We show that the equivariance error of the optimal restoration operator is strictly bounded by the data symmetry error and the discretization mesh size.
  arXiv  Detail & Related papers  (2026-05-13T16:22:19Z)
• Self-Supervised Learning based on Transformed Image Reconstruction for Equivariance-Coherent Feature Representation [3.7622885602373626]
  We propose a self-supervised learning approach to learning computer vision features.<n>The system learns transformations independently by reconstructing images that have undergone previously unseen transformations.<n>Our approach performs strong on a rich set of realistic computer vision downstream tasks, almost always improving over all baselines.
  arXiv  Detail & Related papers  (2025-03-24T15:01:50Z)
• Approximate Equivariance in Reinforcement Learning [35.04248486334824]
  We develop approximately equivariant algorithms in reinforcement learning.<n>Results show that the approximately equivariant network performs on par with exactly equivariant networks when exact symmetries are present.
  arXiv  Detail & Related papers  (2024-11-06T19:44:46Z)
• R2Det: Exploring Relaxed Rotation Equivariance in 2D object detection [26.05910177212846]
  Group Equivariant Convolution (GConv) empowers models to explore underlying symmetry in data, improving performance.<n>We introduce a novel Relaxed Rotation-Equivariant GConv (R2GConv) with only a minimal increase of $4n$ parameters compared to GConv.<n>Based on R2GConv, we propose a Relaxed Rotation-Equivariant Network (R2Net) as the backbone and develop a Relaxed Rotation-Equivariant Object Detector (R2Det) for 2D object detection.
  arXiv  Detail & Related papers  (2024-08-21T16:32:03Z)
• Cross-domain and Cross-dimension Learning for Image-to-Graph Transformers [48.74331852418905]
  Direct image-to-graph transformation is a challenging task that involves solving object detection and relationship prediction in a single model.<n>Due to this task's complexity, large training datasets are rare in many domains, making the training of deep-learning methods challenging.<n>We introduce a set of methods enabling cross-domain and cross-dimension learning for image-to-graph transformers.
  arXiv  Detail & Related papers  (2024-03-11T10:48:56Z)
• Self-supervised Domain-agnostic Domain Adaptation for Satellite Images [18.151134198549574]
  We propose an self-supervised domain-agnostic domain adaptation (SS(DA)2) method to perform domain adaptation without such a domain definition. We first design a contrastive generative adversarial loss to train a generative network to perform image-to-image translation between any two satellite image patches. Then, we improve the generalizability of the downstream models by augmenting the training data with different testing spectral characteristics.
  arXiv  Detail & Related papers  (2023-09-20T07:37:23Z)
• SO(2) and O(2) Equivariance in Image Recognition with Bessel-Convolutional Neural Networks [63.24965775030674]
  This work presents the development of Bessel-convolutional neural networks (B-CNNs) B-CNNs exploit a particular decomposition based on Bessel functions to modify the key operation between images and filters. Study is carried out to assess the performances of B-CNNs compared to other methods.
  arXiv  Detail & Related papers  (2023-04-18T18:06:35Z)
• Imaging with Equivariant Deep Learning [9.333799633608345]
  We review the emerging field of equivariant imaging and show how it can provide improved generalization and new imaging opportunities. We show the interplay between the acquisition physics and group actions and links to iterative reconstruction, blind compressed sensing and self-supervised learning.
  arXiv  Detail & Related papers  (2022-09-05T02:13:57Z)
• Unsupervised Image-to-Image Translation via Pre-trained StyleGAN2 Network [73.5062435623908]
  We propose a new I2I translation method that generates a new model in the target domain via a series of model transformations. By feeding the latent vector into the generated model, we can perform I2I translation between the source domain and target domain.
  arXiv  Detail & Related papers  (2020-10-12T13:51:40Z)
• Asymmetric GANs for Image-to-Image Translation [62.49892218126542]
  Existing models for Generative Adversarial Networks (GANs) learn the mapping from the source domain to the target domain using a cycle-consistency loss. We propose an AsymmetricGAN model with both translation and reconstruction generators of unequal sizes and different parameter-sharing strategy. Experiments on both supervised and unsupervised generative tasks with 8 datasets show that AsymmetricGAN achieves superior model capacity and better generation performance.
  arXiv  Detail & Related papers  (2019-12-14T21:24:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.