Mutual Correlation Network for few-shot learning.

Most metric-based Few-Shot Learning (FSL) methods focus on learning good embeddings of images. However, these methods either lack the ability to explore the cross-correlation (i.e., correlated information) between image pairs or explore limited consensus among the correlation map constrained by the limited receptive field of CNN. We propose a Mutual Correlation Network (MCNet) to explore global consensus among the correlation map by using the self-attention mechanism which has a global receptive field. Our MCNet contains two core modules: (1) a multi-level embedding module that generates multi-level embeddings for an image pair which capture hierarchical semantics, and (2) a mutual correlation module that refines correlation map of two embeddings and generates more robust relational embeddings. Extensive experiments show that our MCNet achieves competitive results on four widely-used few-shot classification benchmarks miniImageNet, tieredImageNet, CUB-200-2011, and CIFAR-FS. Code is available at https://github.com/DRGreat/MCNet.

Empowering the Diversity and Individuality of Option: Residual Soft Option Critic Framework.

Extracting temporal abstraction (option), which empowers the action space, is a crucial challenge in hierarchical reinforcement learning. Under a well-structured action space, decision-making agents can probe more deeply in the searching or plan efficiently through pruning irrelevant action candidates. However, automatically capturing a well-performed temporal abstraction is a nontrivial challenge due to its insufficient exploration and inadequate functionality. We consider alleviating this challenge from two perspectives, i.e., diversity and individuality. For the aspect of diversity, we propose a maximum entropy model based on ensembled options to encourage exploration. For the aspect of individuality, we propose to distinguish each option accurately, utilizing mutual formation minimization, so that each option can better express and function. We name our framework as an ensemble with soft option (ESO) critics. Furthermore, the residual algorithm (RA) with a bidirectional target network is introduced to stabilize bootstrapping, yielding a residual version of ESO. We provide detailed analysis for extensive experiments, which shows that our method boosts performance in commonly used continuous control tasks.

Modeling Hierarchical Uncertainty for Multimodal Emotion Recognition in Conversation.

Approximating the uncertainty of an emotional AI agent is crucial for improving the reliability of such agents and facilitating human-in-the-loop solutions, especially in critical scenarios. However, none of the existing systems for emotion recognition in conversation (ERC) has attempted to estimate the uncertainty of their predictions. In this article, we present HU-Dialogue, which models hierarchical uncertainty for the ERC task. We perturb contextual attention weight values with source-adaptive noises within each modality, as a regularization scheme to model context-level uncertainty and adapt the Bayesian deep learning method to the capsule-based prediction layer to model modality-level uncertainty. Furthermore, a weight-sharing triplet structure with conditional layer normalization is introduced to detect both invariance and equivariance among modalities for ERC. We provide a detailed empirical analysis for extensive experiments, which shows that our model outperforms previous state-of-the-art methods on three popular multimodal ERC datasets.

Impulsive Synchronization of Unbounded Delayed Inertial Neural Networks With Actuator Saturation and Sampled-Data Control and its Application to Image Encryption.

The article considers the impulsive synchronization for inertial neural networks with unbounded delay and actuator saturation via sampled-data control. Based on an impulsive differential inequality, the difficulties caused by unbounded delay and impulsive effect may be effectively avoid. By applying polytopic representation technique, the actuator saturation term is first considered into the design of impulsive controller, and less conservative linear matrix inequality (LMI) criteria that guarantee asymptotical synchronization for the considered model via hybrid control are given. As special cases, the asymptotical synchronization of the considered model via sampled-data control and saturating impulsive control are also studied, respectively. Numerical simulations are presented to claim the effectiveness of theoretical analysis. A new image encryption algorithm is proposed to utilize the synchronization theory of hybrid control. The validity of image encryption algorithm can be obtained by experiments.

Observer-Based Dissipativity Control for T-S Fuzzy Neural Networks With Distributed Time-Varying Delays.

An observer-based dissipativity control for Takagi-Sugeno (T-S) fuzzy neural networks with distributed time-varying delays is studied in this article. First, the network channel delays are modeled as a distributed delay with its kernel. To make full use of kernels of the distributed delay, a Lyapunov-Krasovskii functional (LKF) is established with the kernel of the distributed delay. It is noted that the novel LKF and delay-dependent reciprocally convex inequality plays an important role in dealing with global asymptotical stability and strict (Q, S,R) - α -dissipativity of the T-S fuzzy delayed model. Through the constructed LKF, a new set of less conservative linear matrix inequality (LMI) conditions is presented to obtain an observer-based controller for the T-S fuzzy delayed model. This proposed observer-based controller ensures that the state of the closed-loop system is globally asymptotically stable and strictly (Q, S,R) - α -dissipative. Finally, the effectiveness of the proposed results is shown in numerical simulations.

Impulsive synchronization of coupled delayed neural networks with actuator saturation and its application to image encryption.

The actuator of any physical control systems is constrained by amplitude and energy, which causes the control systems to be inevitably affected by actuator saturation. In this paper, impulsive synchronization of coupled delayed neural networks with actuator saturation is presented. A new controller is designed to introduce actuator saturation term into impulsive controller. Based on sector nonlinearity model approach, impulsive controls with actuator saturation and with partial actuator saturation are studied, respectively, and some effective sufficient conditions are obtained. Numerical simulation is presented to verify the validity of the theoretical analysis results. Finally, the impulsive synchronization is applied to image encryption. The experimental results show that the proposed image encryption system has high security properties.

Synchronization of Delayed Neural Networks via Integral-Based Event-Triggered Scheme.

This article investigates the event-triggered synchronization of delayed neural networks (NNs). A novel integral-based event-triggered scheme (IETS) is proposed where the integral of the system states, and past triggered data over a period of time are used. With the proposed IETS, the integral event-triggered synchronization problem becomes a distributed delay problem. Using the Bessel-Legendre inequalities, sufficient conditions for the existence of a controller that ensures asymptotic synchronization are provided in the form of linear matrix inequalities (LMIs). Illustrative examples are used to demonstrate the advantages of the proposed IETS method over other event-triggered scheme (ETS) methods. Moreover, this IETS method is applied to the image encryption and decryption. A novel encryption algorithm is proposed to enhance the quality of the encryption process.