Task-guided cross-subject latent alignment: a multi-encoder-decoder VAE

• URL: http://arxiv.org/abs/2606.15989v1
• Date: Sun, 14 Jun 2026 19:30:11 GMT
• Title: Task-guided cross-subject latent alignment: a multi-encoder-decoder VAE
• Abstract summary: We introduce a Multi-Encoder-Decoder Variational Autoencoder (MED-VAE) that achieves cross-subject alignment without shared stimuli.<n>We show that MED-VAE creates common latent spaces with superior semantic organisation, achieving higher cross-subject alignment than common methods.<n>We also show that this superior alignment directly enables cross-subject neural prediction, as demonstrated via cross-subject image decoding.
• Score: 1.936594951038608
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Aligning neural activity across subjects offers the promise of discovering shared computational principles and generalizable decoders. However, traditional alignment methods require shared stimuli across subjects, a constraint that limits applicability to naturalistic paradigms with limited or non-overlapping data. We introduce a Multi-Encoder-Decoder Variational Autoencoder (MED-VAE) that achieves cross-subject alignment without shared stimuli by anchoring representations to a common scaffold provided by a pretrained ANN. Using the Natural Scenes Dataset, we show that MED-VAE creates common latent spaces with superior semantic organisation, achieving higher cross-subject alignment than common methods while maintaining robust generalisation to held-out stimuli where traditional methods degrade. Reconstructing from these common spaces back to each subject's original neural space, MED-VAE preserves equal stimulus-driven signal in its cross-subject latent space. Finally, we show that this superior alignment directly enables cross-subject neural prediction, as demonstrated via cross-subject image decoding. In summary, we introduce a framework to identify generalisable common subspaces for cross-subject predictions and downstream tasks, demonstrated here for visual cortex responses to static images.

Related papers

• Warp-free Cross-view Geo-localization via Feature-space Consensus Mining [49.843311525678565]
  Cross-view geo-localization is challenging due to drastic viewpoint changes and large appearance discrepancies between street-level and satellite imagery.<n>We propose a novel joint-view consensus-guided learning framework that entirely bypasses explicit geometric warping.
  arXiv  Detail & Related papers  (2026-08-10T09:02:06Z)
• Cross-Subject Semantic Decoding with Shared-Space Alignment for Generalized Neural Representation Learning [80.18561652862725]
  Generalizing across subjects remains challenging in invasive neural recordings.<n>We propose a cross-subject semantic decoding framework that aligns neural responses to speech perception from multiple subjects into a shared latent space.
  arXiv  Detail & Related papers  (2026-07-03T03:59:16Z)
• SGHA-Attack: Semantic-Guided Hierarchical Alignment for Transferable Targeted Attacks on Vision-Language Models [73.19044613922911]
  Large vision-language models (VLMs) are vulnerable to transfer-based adversarial perturbations.<n>We propose SGHA-Attack, a framework that adopts multiple target references and enforces intermediate-layer consistency.<n>Experiments on open-source and commercial black-box VLMs show that SGHA-Attack achieves stronger targeted transferability than prior methods.
  arXiv  Detail & Related papers  (2026-02-02T03:10:41Z)
• Cross-Layer Discrete Concept Discovery for Interpreting Language Models [13.842670153893977]
  Cross-layer VQ-VAE is a framework that uses vector quantization to map representations across layers.<n>Our approach uniquely combines top-k temperature-based sampling during quantization with EMA codebook updates.
  arXiv  Detail & Related papers  (2025-06-24T22:43:36Z)
• Unified Domain Adaptive Semantic Segmentation [105.05235403072021]
  Unsupervised Adaptive Domain Semantic (UDA-SS) aims to transfer the supervision from a labeled source domain to an unlabeled target domain.<n>We propose a Quad-directional Mixup (QuadMix) method, characterized by tackling distinct point attributes and feature inconsistencies.<n>Our method outperforms the state-of-the-art works by large margins on four challenging UDA-SS benchmarks.
  arXiv  Detail & Related papers  (2023-11-22T09:18:49Z)
• Ensuring Topological Data-Structure Preservation under Autoencoder Compression due to Latent Space Regularization in Gauss--Legendre nodes [0.0]
  We prove that regularised autoencoders ensure a one-to-one re-embedding of the initial data manifold to its latent representation. This observation extends through the classic FashionMNIST dataset up to real world encoding problems for MRI brain scans.
  arXiv  Detail & Related papers  (2023-09-15T07:58:26Z)
• Stacked Hybrid-Attention and Group Collaborative Learning for Unbiased Scene Graph Generation [62.96628432641806]
  Scene Graph Generation aims to first encode the visual contents within the given image and then parse them into a compact summary graph. We first present a novel Stacked Hybrid-Attention network, which facilitates the intra-modal refinement as well as the inter-modal interaction. We then devise an innovative Group Collaborative Learning strategy to optimize the decoder.
  arXiv  Detail & Related papers  (2022-03-18T09:14:13Z)
• Learning Aligned Cross-Modal Representation for Generalized Zero-Shot Classification [17.177622259867515]
  We propose an innovative autoencoder network by learning Aligned Cross-Modal Representations (dubbed ACMR) for Generalized Zero-Shot Classification (GZSC) Specifically, we propose a novel Vision-Semantic Alignment (VSA) method to strengthen the alignment of cross-modal latent features on the latent subspaces guided by a learned classifier. In addition, we propose a novel Information Enhancement Module (IEM) to reduce the possibility of latent variables collapse meanwhile encouraging the discriminative ability of latent variables.
  arXiv  Detail & Related papers  (2021-12-24T03:35:37Z)
• InteL-VAEs: Adding Inductive Biases to Variational Auto-Encoders via Intermediary Latents [60.785317191131284]
  We introduce a simple and effective method for learning VAEs with controllable biases by using an intermediary set of latent variables. In particular, it allows us to impose desired properties like sparsity or clustering on learned representations. We show that this, in turn, allows InteL-VAEs to learn both better generative models and representations.
  arXiv  Detail & Related papers  (2021-06-25T16:34:05Z)
• TraND: Transferable Neighborhood Discovery for Unsupervised Cross-domain Gait Recognition [77.77786072373942]
  This paper proposes a Transferable Neighborhood Discovery (TraND) framework to bridge the domain gap for unsupervised cross-domain gait recognition. We design an end-to-end trainable approach to automatically discover the confident neighborhoods of unlabeled samples in the latent space. Our method achieves state-of-the-art results on two public datasets, i.e., CASIA-B and OU-LP.
  arXiv  Detail & Related papers  (2021-02-09T03:07:07Z)
• Closed-Form Factorization of Latent Semantics in GANs [65.42778970898534]
  A rich set of interpretable dimensions has been shown to emerge in the latent space of the Generative Adversarial Networks (GANs) trained for synthesizing images. In this work, we examine the internal representation learned by GANs to reveal the underlying variation factors in an unsupervised manner. We propose a closed-form factorization algorithm for latent semantic discovery by directly decomposing the pre-trained weights.
  arXiv  Detail & Related papers  (2020-07-13T18:05:36Z)

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.