Neural Networks

Explainable artificial intelligence (XAI) has been increasingly investigated to enhance the transparency of black-box artificial intelligence models, promoting better user understanding and trust. Developing an XAI that is faithful to models and ...

• Human attention guided XAI is proposed for more faithful and plausible explanations.
• Two gradient-based XAI methods are presented for explaining object detection models.
• Human attention is adopted as an objective plausibility ...

Transformer-based image denoising methods have shown remarkable potential but suffer from high computational cost and large memory footprint due to their linear operations for capturing long-range dependencies. In this work, we aim to develop a ...

• We propose a novel Efficient Wavelet-Transformer (EWT) for SID.
• EWT increases the speed of Transformer by more than 80% and reduces GPU by more than 60%.
• We propose an efficient Multi-level Feature Aggregation Module (MFAM).
• We ...

Occluded person re-identification (Re-ID) is a challenging task, as pedestrians are often obstructed by various occlusions, such as non-pedestrian objects or non-target pedestrians. Previous methods have heavily relied on auxiliary models to ...

• A MLRAT model is introduced to learn robust feature representations.
• A PLRA module is designed to identify key patches and perform local feature learning.
• An SLRA module is presented to learn discriminative features.

In modern Reinforcement Learning (RL) approaches, optimizing the Bellman error is a critical element across various algorithms, notably in deep Q-Learning and related methodologies. Traditional approaches predominantly employ the mean-squared ...

• We challenge the belief in a Normally distributed Bellman error with a Logistic distribution.
• We explore Logistic distribution sampling error using Bias-Variance decomposition for optimal batch size.
• We confirm the Logistic ...

Wide deployment of deep neural networks (DNNs) based applications (e.g., style transfer, cartoonish), stimulating the need for copyright protection of such application’s production. Though some traditional visible copyright techniques exist, they ...

In scenarios like privacy protection or large-scale data transmission, data-free knowledge distillation (DFKD) methods are proposed to learn Knowledge Distillation (KD) when data is not accessible. They generate pseudo samples by extracting the ...

Aspect Sentiment Triple Extraction (ASTE), a subtask of fine-grained sentiment analysis, aims to extract aspect terms, opinion terms, and their corresponding sentiment polarities from sentences. Previous methods often enumerated all possible ...

This paper investigates the optimal tracking issue for continuous-time (CT) nonlinear asymmetric constrained zero-sum games (ZSGs) by exploiting the neural critic technique. Initially, an improved algorithm is constructed to tackle the tracking ...

Graph Neural Networks (GNNs) have demonstrated remarkable success in graph node classification task. However, their performance heavily relies on the availability of high-quality labeled data, which can be time-consuming and labor-intensive to ...

To enhance the model’s generalization ability in unsupervised domain adaptive segmentation tasks, most approaches have primarily focused on pixel-level local features, but neglected the clue in category information. This limitation results in the ...

• A UDA algorithm is proposed to leverage the clue provided by category information.
• The strategy aids in learning category-discriminative feature representations.
• The mixed domain can further improve the effectiveness of adversarial ...

Missing modality sentiment analysis is a prevalent and challenging issue in real life. Furthermore, the heterogeneity of multimodality often leads to an imbalance in optimization when attempting to optimize the same objective across all ...

• This paper establishes a connection between the study of full and missing modality.
• A sample-weighted distillation strategy is employed to adapt to missing modality.
• A regularization network is proposed to mitigate the optimization ...

Multi-source unsupervised domain adaptation aims to transfer knowledge from multiple labeled source domains to an unlabeled target domain. Existing methods either seek a mixture of distributions across various domains or combine multiple single-...

Knowledge graph embedding (KGE) involves mapping entities and relations to low-dimensional dense embeddings, enabling a wide range of real-world applications. The mapping is achieved via distinguishing the positive and negative triplets in ...

In this work we approach attractor neural networks from a machine learning perspective: we look for optimal network parameters by applying a gradient descent over a regularized loss function. Within this framework, the optimal neuron-interaction ...

Image super-resolution (ISR) is designed to recover lost detail information from low-resolution images, resulting in high-quality and high-definition high-resolution images. In the existing single ISR (SISR) methods based on convolutional neural ...

While computer vision has proven valuable for medical image segmentation, its application faces challenges such as limited dataset sizes and the complexity of effectively leveraging unlabeled images. To address these challenges, we present a ...

Image content identification systems have many applications in industry and academia. In particular, a hash-based content identification system uses a robust image hashing function that computes a short binary identifier summarizing the ...

The circuitry and pathways in the brains of humans and other species have long inspired researchers and system designers to develop accurate and efficient systems capable of solving real-world problems and responding in real-time. We propose the ...

Homeostasis is a self-regulatory process, wherein an organism maintains a specific internal physiological state. Homeostatic reinforcement learning (RL) is a framework recently proposed in computational neuroscience to explain animal behavior. ...

• We conducted a direct optimization of the simulated embodied agent for homeostasis.
• Agents obtained motor control for behavioral homeostasis in various environments.
• Interoception-dependent foraging emerged in an environment with ...

Although existing reconstruction-based multivariate time series anomaly detection (MTSAD) methods have shown advanced performance, most assume the training data is clean. When faced with noise or contamination in training data, they can also ...

• A robust multi-scale feature extraction framework is proposed.
• A global-local dual memory-augmented autoencoder is proposed.
• A local memory module based on common features of neighboring windows is proposed.

Fluid motion can be considered as a point cloud transformation when using the SPH method. Compared to traditional numerical analysis methods, using machine learning techniques to learn physics simulations can achieve near-accurate results, while ...

• Two key aspects of neural fluid simulation: global control and physical law adherence.
• Novel Dual-pipeline network balances global control and physics adherence.
• Attention-based module integrates dual pathways for optimal network ...

Due to increasing interest in adapting models on resource-constrained edges, parameter-efficient transfer learning has been widely explored. Among various methods, Visual Prompt Tuning (VPT), prepending learnable prompts to input space, shows ...