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3D scene understanding is crucial for facilitating seamless interaction between digital devices and the physical world. Real-time capturing and processing of the 3D scene are essential for achieving this seamless integration. While existing approaches typically separate acquisition and processing for each frame, the advent of resolution-scalable 3D sensors offers an opportunity to overcome this paradigm and fully leverage the otherwise wasted acquisition time to initiate processing.

Deep learning in image classification has achieved remarkable success but at the cost of high resource demands. Model compression through automatic joint pruning-quantization addresses this issue, yet most existing techniques overlook a critical aspect: layer correlations. These correlations are essential as they expose redundant computations across layers, and leveraging them facilitates efficient design space exploration. This study employs Graph Neural Networks (GNN) to learn these inter-layer relationships, thereby optimizing the pruning-quantization strategy for the targeted model.

Deep Metric Learning (DML) based on Convolutional Neural Networks (CNNs) is vulnerable to adversarial attacks. Adversarial training, where adversarial samples are generated at each iteration, is one of the prominent defense techniques for robust DML. However, adversarial training increases computational complexity and causes a trade-off between robustness and generalization. This study proposes a lightweight, robust DML framework that learns a non-linear projection to map the embeddings of a CNN into an adversarially robust space.

Deepfake detection is critical in mitigating the societal threats posed by manipulated videos. While various algorithms have been developed for this purpose, challenges arise when detectors operate externally, such as on smartphones, when users take a photo of deepfake images and upload on the Internet. One significant challenge in such scenarios is the presence of Moire patterns, which degrade image quality and confound conventional classification algorithms, including deep neural networks (DNNs). The impact of Moire patterns remains largely unexplored for deepfake detectors.

The emerging deep learning algorithms have shown significant potential in the development of efficient computer aided diagnosis tools for automated detection of lung infections using chest radiographs. However, many existing methods are slice-based and require manual selection of appropriate slices from the entire CT scan, which is tedious and requires expert radiologists.

Recent advancements in artificial intelligence algorithms for medical imaging show significant potential in automating the detection of lung infections from chest radiograph scans. However, current approaches often focus solely on either 2-D or 3-D scans, failing to leverage the combined advantages of both modalities. Moreover, conventional slice-based methods place a manual burden on radiologists for slice selection.

In this work, we propose a semi-supervised deep feature generation network that accounts for local similarities. It is based on the deep dictionary learning (DDL) framework. The formulation accounts for two unique aspects of hyperspectral classification. First, the fact that the total number of pixels / samples to be labeled is constant; this allows for a semi-supervised formulation allowing only a few pixels / samples to be labeled as training data. Second, the samples / pixels are spatially correlated; this leads to a graph regularization formulation.

Although transformer-based models have improved the state-of-the-art in speech recognition, it is still not well understood what information from the speech signal these models encode in their latent representations. This study investigates the potential of using labelled data (TIMIT) to probe wav2vec 2.0 embeddings for insights into the encoding and visualisation of speech signal information at phone boundaries. Our experiment involves training probing models to detect phone-specific articulatory features in the hidden layers based on IPA classifications.

To alleviate the negative impacts of noisy labels, most of the noisy label learning (NLL) methods dynamically divide the training data into two types, “clean samples” and “noisy samples”, in the training process. However, the conventional selection of clean samples heavily depends on the features learned in the early stages of training, making it difficult to guarantee the cleanliness of the selected samples in scenarios where the noise ratio is high.

Although BP decoders are efficient and provide significant
performance for classical low-density parity-check (LDPC)
codes, they will suffer a degradation in performance for quantum
LDPC (QLDPC) codes due to the limitations in the quantum
field. In this paper, we propose a posterior adjustment of
either a single qubit or multiple qubits within binary belief
propagation (BP). The adjustment process changes the posterior
likelihood ratio for one or multiple qubits according to the