DSS-GAN: Directional State Space GAN with Mamba backbone for Class-Conditional Image Synthesis

• URL: http://arxiv.org/abs/2603.17637v1
• Date: Wed, 18 Mar 2026 11:58:47 GMT
• Title: DSS-GAN: Directional State Space GAN with Mamba backbone for Class-Conditional Image Synthesis
• Abstract summary: We present DSS-GAN, the first generative adversarial network to employ Mamba as a hierarchical generator backbone for noise-to-image synthesis.<n>The central contribution is Directional Latent (DLR), a novel conditioning mechanism that decomposes the latent vector into direction-specific subvectors.
• Score: 41.99844472131922
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We present DSS-GAN, the first generative adversarial network to employ Mamba as a hierarchical generator backbone for noise-to-image synthesis. The central contribution is Directional Latent Routing (DLR), a novel conditioning mechanism that decomposes the latent vector into direction-specific subvectors, each jointly projected with a class embedding to produce a feature-wise affine modulation of the corresponding Mamba scan. Unlike conventional class conditioning that injects a global signal, DLR couples class identity and latent structure along distinct spatial axes of the feature map, applied consistently across all generative scales. DSS-GAN achieves improved FID, KID, and precision-recall scores compared to StyleGAN2-ADA across multiple tested datasets. Analysis of the latent space reveals that directional subvectors exhibit measurable specialization: perturbations along individual components produce structured, direction-correlated changes in the synthesized image.

Related papers

• Direction-adaptive Mamba: Spatial-Frequency Dual-Domain Collaborative Learning for PolSAR Image Classification [36.750686260055424]
  Deep learning dominates polarimetric synthetic aperture radar (PolSAR) image classification.<n>Mamba architectures serve as favorable backbones due to linear complexity and strong global modeling capacity.<n>This work proposes DA-Mamba, a direction-adaptive Mamba framework with dual-domain collaborative learning for PolSAR classification.
  arXiv  Detail & Related papers  (2026-07-26T05:17:07Z)
• Resolving Representation Ambiguity in Feedforward Novel View Synthesis Transformer via Semantic-Spatial Decoupling [117.72367737965048]
  Transformer-based models have advanced feedforward novel view synthesis (NVS)<n>Current architectures mix semantic information (e.g., RGB) and spatial information (e.g., Plcker rays) into a shared feature space.<n>We propose to decouple the representation of feedforward NVS transformers into separate semantic and spatial tokens.
  arXiv  Detail & Related papers  (2026-05-18T16:09:52Z)
• OmniLiDAR: A Unified Diffusion Framework for Multi-Domain 3D LiDAR Generation [100.36917520403227]
  We present a unified text-conditioned diffusion framework that generates LiDAR scans in a shared range-image representation across eight representative domains.<n>In the absence of a public consolidated benchmark, we construct an 8-domain dataset by combining real-world scans with physically based weather simulation and systematic beam reduction.
  arXiv  Detail & Related papers  (2026-05-13T17:42:20Z)
• Implicit Neural Representations: A Signal Processing Perspective [54.57279006229212]
  Implicit neural representations (INRs) mark a fundamental shift in signal modeling, moving from discrete sampled data to continuous functional representations.<n>This article examines the evolution of INRs from a signal processing perspective, emphasizing spectral behavior, sampling theory, and multiscale representation.<n>We highlight the utility of INRs across a broad range of applications, including inverse problems in medical and radar imaging, compression, and 3D scene representation.
  arXiv  Detail & Related papers  (2026-04-16T14:12:06Z)
• One CT Unified Model Training Framework to Rule All Scanning Protocols [32.68329101435685]
  Non-ideal measurement computed tomography (NICT) is expanding the clinical use of CT.<n>Most methods require paired data, which is an impractical demand due to inevitable organ motion.<n>We propose an Uncertainty-Guided Manifold Smoothing (UMS) framework to bridge the gaps between sub-manifolds.
  arXiv  Detail & Related papers  (2026-03-16T09:26:32Z)
• SKANet: A Cognitive Dual-Stream Framework with Adaptive Modality Fusion for Robust Compound GNSS Interference Classification [47.20483076887704]
  Global Navigation Satellite Systems (GNSS) face growing threats from sophisticated jamming interference.<n>We propose a cognitive deep learning framework built upon a dual-stream architecture that integrates Time-Frequency Images (TFIs) and Power Spectral Density (PSD)<n>We show that SKANet achieves an overall accuracy of 96.99%, exhibiting superior robustness for compound jamming classification.
  arXiv  Detail & Related papers  (2026-01-19T07:42:45Z)
• Pyramidal Adaptive Cross-Gating for Multimodal Detection [0.0]
  PACGNet is an architecture designed to perform deep fusion within the backbone.<n>The P module reconstructs the feature hierarchy via a progressive hierarchical gating mechanism.<n>Our PACGNet sets a new state-of-the-art benchmark, with mAP50 scores reaching 81.7% and 82.1% respectively.
  arXiv  Detail & Related papers  (2025-12-20T09:32:18Z)
• Bidirectional Representations Augmented Autoregressive Biological Sequence Generation:Application in De Novo Peptide Sequencing [51.12821379640881]
  Autoregressive (AR) models offer holistic, bidirectional representations but face challenges with generative coherence and scalability.<n>We propose a hybrid framework enhancing AR generation by dynamically integrating rich contextual information from non-autoregressive mechanisms.<n>A novel cross-decoder attention module enables the AR decoder to iteratively query and integrate these bidirectional features.
  arXiv  Detail & Related papers  (2025-10-09T12:52:55Z)
• SoundCompass: Navigating Target Sound Extraction With Effective Directional Clue Integration In Complex Acoustic Scenes [13.103199041728303]
  Recent advances in target sound extraction (TSE) utilize directional clues derived from direction of arrival (DoA)<n>We propose Sound, an effective directional clue integration framework centered on a Spectral Pairwise INteraction (SPIN) module.<n>SPIN captures cross-channel spatial correlations in the complex spectrogram domain to preserve full spatial information in multichannel signals.
  arXiv  Detail & Related papers  (2025-09-23T02:36:39Z)
• Unified CNNs and transformers underlying learning mechanism reveals multi-head attention modus vivendi [0.0]
  Convolutional neural networks (CNNs) evaluate short-range correlations in input images which progress along the layers.<n> vision transformer (ViT) architectures evaluate long-range correlations, using repeated transformer encoders composed of fully connected layers.<n>This study demonstrates that CNNs and ViT architectures stem from a unified underlying learning mechanism.
  arXiv  Detail & Related papers  (2025-01-22T14:19:48Z)
• S^2Former-OR: Single-Stage Bi-Modal Transformer for Scene Graph Generation in OR [50.435592120607815]
  Scene graph generation (SGG) of surgical procedures is crucial in enhancing holistically cognitive intelligence in the operating room (OR) Previous works have primarily relied on multi-stage learning, where the generated semantic scene graphs depend on intermediate processes with pose estimation and object detection. In this study, we introduce a novel single-stage bi-modal transformer framework for SGG in the OR, termed S2Former-OR.
  arXiv  Detail & Related papers  (2024-02-22T11:40:49Z)
• GSMFlow: Generation Shifts Mitigating Flow for Generalized Zero-Shot Learning [55.79997930181418]
  Generalized Zero-Shot Learning aims to recognize images from both the seen and unseen classes by transferring semantic knowledge from seen to unseen classes. It is a promising solution to take the advantage of generative models to hallucinate realistic unseen samples based on the knowledge learned from the seen classes. We propose a novel flow-based generative framework that consists of multiple conditional affine coupling layers for learning unseen data generation.
  arXiv  Detail & Related papers  (2022-07-05T04:04:37Z)

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.