- Bayesian learning; Bayesian signal processing (MLR-BAYL)
- Bounds on performance (MLR-PERF)
- Applications in Systems Biology (MLR-SYSB)
- Applications in Music and Audio Processing (MLR-MUSI)
- Applications in Data Fusion (MLR-FUSI)
- Cognitive information processing (MLR-COGP)
- Distributed and Cooperative Learning (MLR-DIST)
- Learning theory and algorithms (MLR-LEAR)
- Neural network learning (MLR-NNLR)
- Information-theoretic learning (MLR-INFO)
- Independent component analysis (MLR-ICAN)
- Graphical and kernel methods (MLR-GRKN)
- Other applications of machine learning (MLR-APPL)
- Pattern recognition and classification (MLR-PATT)
- Source separation (MLR-SSEP)
- Sequential learning; sequential decision methods (MLR-SLER)
- Read more about BigVSAN: Enhancing GAN-based Neural Vocoders with Slicing Adversarial Network
- Log in to post comments
Generative adversarial network (GAN)-based vocoders have been intensively studied because they can synthesize high-fidelity audio waveforms faster than real-time. However, it has been reported that most GANs fail to obtain the optimal projection for discriminating between real and fake data in the feature space. In the literature, it has been demonstrated that slicing adversarial network (SAN), an improved GAN training framework that can find the optimal projection, is effective in the image generation task. In this paper, we investigate the effectiveness of SAN in the vocoding task.
- Categories:
- Read more about Source-Free Online Domain Adaptive Semantic Segmentation of Satellite Images under Image Degradation
- Log in to post comments
Online adaptation to distribution shifts in satellite image segmentation stands as a crucial yet underexplored problem. In this paper, we address source-free and online domain adaptation, i.e., test-time adaptation (TTA), for satellite images, with the focus on mitigating distribution shifts caused by various forms of image degradation. Towards achieving this goal, we propose a novel TTA approach involving two effective strategies. First, we progressively estimate the global Batch Normalization (BN) statistics of the target distribution with incoming data stream.
- Categories:
- Read more about DATA-SCARCE CONDITION MODELING REQUIRES MODEL-BASED PRIOR REGULARIZATION
- Log in to post comments
In the metallurgical industry, taking measurements during production can be infeasible or undesired, and only the terminated process can be measured. This poses problems for regression models, as the intermediate target values for a time series are hidden in the accumulated end-of-process measurement. The lack of data quality and quantity also often limits the modeling to linear estimators, as neural networks struggle to converge and/or overfit on scarce noisy data.
- Categories:
- Read more about Convolutional Filters and Neural Networks with Non Commutative Algebras
- Log in to post comments
In this paper we introduce and study the algebraic generalization of non commutative convolutional neural networks. We leverage the theory of algebraic signal processing to model convolutional non commutative architectures, and we derive concrete stability bounds that extend those obtained in the literature for commutative convolutional neural networks. We show that non commutative convolutional architectures can be stable to deformations on the space of operators.
- Categories:
- Read more about Non Commutative Convolutional Signal Models in Neural Networks: Stability to Small Deformations
- Log in to post comments
In this paper we discuss the results recently published in [1] about algebraic signal models (ASMs) based on non commutative algebras and their use in convolutional neural networks. Relying on the general tools from algebraic signal processing (ASP), we study the filtering and stability properties of non commutative convolutional filters. We show how non commutative filters can be stable to small perturbations on the space of operators.
- Categories:
- Read more about Hyperspectral Image Reconstruction using Hierarchical Neural Architecture Search from a Snapshot Image
- Log in to post comments
Hyperspectral imaging is a promising imaging modality, and has attracted increasing research attention by compressive sensing such as coded aperture snapshot spectral imaging (CASSI), for simultaneously capturing abundant information in spatial, spectral and temporal domains. Hyperspectral image (HSI) reconstruction in the CASSI aims to retrieve the original 3D signal upon the 2D compressed snapshot.
ID6267.pdf
- Categories:
- Read more about Deep Versatile Hyperspectral Reconstruction Model from a Snapshot Measurement with Arbitrary Masks
- 1 comment
- Log in to post comments
Recently, coded aperture snapshot spectral imaging (CASSI) has been actively researched to capture three-dimensional (3D) hyperspectral (HS) images for dynamic scenes, where the optical systems detect a 2D snapshot measurement while a computational algorithm performs the inverse problem for recovering the latent HS cubic data. Benefiting from the powerful modeling capability of the deep convolution neural networks (DCNN), the reconstruction performance of the HS images has been significantly improved.
ID1717.pdf
- Categories:
- Read more about RADAR PERCEPTION WITH SCALABLE CONNECTIVE TEMPORAL RELATIONS FOR AUTONOMOUS DRIVING
- Log in to post comments
Due to the noise and low spatial resolution in automotive radar data, exploring temporal relations of learnable features over consecutive 2 radar frames has shown performance gain on downstream tasks (e.g., object detection and tracking) in our previous study. In this paper, we further enhance radar perception by significantly extending the time horizon of temporal relations.
- Categories:
- Read more about FINCGAN: A GAN FRAMEWORK OF IMBALANCED NODE CLASSIFICATION ON HETEROGENEOUS GRAPH NEURAL NETWORK
- Log in to post comments
We introduce FincGAN, a GAN framework designed to address the class imbalance in GNNs by enhancing minority sample synthesis and ensuring connectivity with sparsity-aware edge generators.
- Categories:
- Read more about Improving Continual Learning of Acoustic Scene Classification via Mutual Information Optimization
- Log in to post comments
Continual learning, which aims to incrementally accumulate knowledge, has been an increasingly significant but challenging research topic for deep models that are prone to catastrophic forgetting. In this paper, we propose a novel replay-based continual learning approach in the context of class-incremental learning in acoustic scene classification, to classify audio recordings into an expanding set of classes that characterize the acoustic scenes. Our approach is improving both the modeling and memory selection mechanism via mutual information optimization in continual learning.
- Categories: