mailitics

Category: cs.CV

  • Geometric structures and deviations on James’ symmetric positive-definite matrix bicone domain

    Geometric structures and deviations on James’ symmetric positive-definite matrix bicone domain arXiv:2603.02483v1 Announce Type: new Abstract: Symmetric positive-definite (SPD) matrix datasets play a central role across numerous scientific disciplines, including signal processing, statistics, finance, computer vision, information theory, and machine learning among others. The set of SPD matrices forms a cone which can be viewed…

  • Aligned explanations in neural networks

    Aligned explanations in neural networks arXiv:2601.04378v1 Announce Type: cross Abstract: Feature attribution is the dominant paradigm for explaining deep neural networks. However, most existing methods only loosely reflect the model’s prediction-making process, thereby merely white-painting the black box. We argue that explanatory alignment is a key aspect of trustworthiness in prediction tasks: explanations must be…

  • Residual Prior Diffusion: A Probabilistic Framework Integrating Coarse Latent Priors with Diffusion Models

    Residual Prior Diffusion: A Probabilistic Framework Integrating Coarse Latent Priors with Diffusion Models arXiv:2512.21593v1 Announce Type: new Abstract: Diffusion models have become a central tool in deep generative modeling, but standard formulations rely on a single network and a single diffusion schedule to transform a simple prior, typically a standard normal distribution, into the target…

  • Automated Pollen Recognition in Optical and Holographic Microscopy Images

    Automated Pollen Recognition in Optical and Holographic Microscopy Images arXiv:2512.08589v1 Announce Type: cross Abstract: This study explores the application of deep learning to improve and automate pollen grain detection and classification in both optical and holographic microscopy images, with a particular focus on veterinary cytology use cases. We used YOLOv8s for object detection and MobileNetV3L…

  • Spatiotemporal Pyramid Flow Matching for Climate Emulation

    Spatiotemporal Pyramid Flow Matching for Climate Emulation arXiv:2512.02268v1 Announce Type: cross Abstract: Generative models have the potential to transform the way we emulate Earth’s changing climate. Previous generative approaches rely on weather-scale autoregression for climate emulation, but this is inherently slow for long climate horizons and has yet to demonstrate stable rollouts under nonstationary forcings.…

  • Non-Negative Stiefel Approximating Flow: Orthogonalish Matrix Optimization for Interpretable Embeddings

    Non-Negative Stiefel Approximating Flow: Orthogonalish Matrix Optimization for Interpretable Embeddings arXiv:2511.06425v1 Announce Type: new Abstract: Interpretable representation learning is a central challenge in modern machine learning, particularly in high-dimensional settings such as neuroimaging, genomics, and text analysis. Current methods often struggle to balance the competing demands of interpretability and model flexibility, limiting their effectiveness in…

  • Kernel VICReg for Self-Supervised Learning in Reproducing Kernel Hilbert Space

    Kernel VICReg for Self-Supervised Learning in Reproducing Kernel Hilbert Space arXiv:2509.07289v1 Announce Type: new Abstract: Self-supervised learning (SSL) has emerged as a powerful paradigm for representation learning by optimizing geometric objectives–such as invariance to augmentations, variance preservation, and feature decorrelation–without requiring labels. However, most existing methods operate in Euclidean space, limiting their ability to capture…

  • Membership Inference Attacks with False Discovery Rate Control

    Membership Inference Attacks with False Discovery Rate Control arXiv:2508.07066v1 Announce Type: new Abstract: Recent studies have shown that deep learning models are vulnerable to membership inference attacks (MIAs), which aim to infer whether a data record was used to train a target model or not. To analyze and study these vulnerabilities, various MIA methods have…

  • Flow Stochastic Segmentation Networks

    Flow Stochastic Segmentation Networks arXiv:2507.18838v1 Announce Type: cross Abstract: We introduce the Flow Stochastic Segmentation Network (Flow-SSN), a generative segmentation model family featuring discrete-time autoregressive and modern continuous-time flow variants. We prove fundamental limitations of the low-rank parameterisation of previous methods and show that Flow-SSNs can estimate arbitrarily high-rank pixel-wise covariances without assuming the rank…

  • Conformal Prediction for Long-Tailed Classification

    Conformal Prediction for Long-Tailed Classification arXiv:2507.06867v1 Announce Type: new Abstract: Many real-world classification problems, such as plant identification, have extremely long-tailed class distributions. In order for prediction sets to be useful in such settings, they should (i) provide good class-conditional coverage, ensuring that rare classes are not systematically omitted from the prediction sets, and (ii)…

  • Conformal Object Detection by Sequential Risk Control

    Conformal Object Detection by Sequential Risk Control arXiv:2505.24038v1 Announce Type: new Abstract: Recent advances in object detectors have led to their adoption for industrial uses. However, their deployment in critical applications is hindered by the inherent lack of reliability of neural networks and the complex structure of object detection models. To address these challenges, we…

  • A Mathematical Perspective On Contrastive Learning

    A Mathematical Perspective On Contrastive Learning arXiv:2505.24134v1 Announce Type: new Abstract: Multimodal contrastive learning is a methodology for linking different data modalities; the canonical example is linking image and text data. The methodology is typically framed as the identification of a set of encoders, one for each modality, that align representations within a common latent…

  • Feature Representation Transferring to Lightweight Models via Perception Coherence

    Feature Representation Transferring to Lightweight Models via Perception Coherence arXiv:2505.06595v1 Announce Type: new Abstract: In this paper, we propose a method for transferring feature representation to lightweight student models from larger teacher models. We mathematically define a new notion called textit{perception coherence}. Based on this notion, we propose a loss function, which takes into account…

  • DQA: An Efficient Method for Deep Quantization of Deep Neural Network Activations

    DQA: An Efficient Method for Deep Quantization of Deep Neural Network Activations arXiv:2412.09687v1 Announce Type: cross Abstract: Quantization of Deep Neural Network (DNN) activations is a commonly used technique to reduce compute and memory demands during DNN inference, which can be particularly beneficial on resource-constrained devices. To achieve high accuracy, existing methods for quantizing activations…

  • Generalized Least Squares Kernelized Tensor Factorization

    Generalized Least Squares Kernelized Tensor Factorization arXiv:2412.07041v1 Announce Type: new Abstract: Real-world datasets often contain missing or corrupted values. Completing multidimensional tensor-structured data with missing entries is essential for numerous applications. Smoothness-constrained low-rank factorization models have shown superior performance with reduced computational costs. While effective at capturing global and long-range correlations, these models struggle to…