Employing a novel theoretical framework, this article delves into the forgetting characteristics of GRM-based learning systems, pinpointing the forgetting process as a rise in the model's risk encountered during training. While recent applications of GANs have produced high-quality generative replay samples, their applicability is predominantly limited to subsequent tasks, constrained by the absence of an effective inference pipeline. Motivated by the theoretical underpinnings and seeking to overcome the limitations of current methods, we introduce the lifelong generative adversarial autoencoder (LGAA). LGAA's design incorporates a generative replay network and three inference models, each uniquely tasked with the inference of a particular latent variable type. In experiments, LGAA exhibited the ability to learn novel visual concepts while retaining prior knowledge. This property makes it suitable for a wide range of downstream tasks.
To forge a formidable classifier ensemble, the base classifiers must exhibit both accuracy and a wide spectrum of capabilities. Although, a unified standard for the definition and measurement of diversity is not in place. This paper presents learners' interpretability diversity (LID), a new approach to measuring the diversity of machine learning models that are interpretable. The ensuing action is the proposition of a LID-based classifier ensemble. An innovative aspect of this ensemble concept is its application of interpretability to quantify diversity, which precedes the assessment of the divergence between two interpretable base learners prior to training. selleck chemicals In order to confirm the performance of the proposed method, we employed a decision-tree-initialized dendritic neuron model (DDNM) as the baseline learner within the ensemble architecture. Our application is tested across seven benchmark datasets. The DDNM ensemble, augmented by LID, demonstrates superior accuracy and computational efficiency compared to prevalent classifier ensembles, as evidenced by the results. The dendritic neuron model, initialized by a random forest and employing LID, is a standout representative of the DDNM ensemble.
Word representations, often endowed with rich semantic properties culled from extensive corpora, are widely employed in diverse natural language applications. Deep language models, using dense word representations as their foundation, are computationally expensive and consume vast amounts of memory. Neuromorphic computing systems, drawing inspiration from the brain and boasting enhanced biological interpretability and reduced energy consumption, nonetheless confront significant hurdles in representing words through neuronal activity, thereby limiting their applicability to more intricate downstream language tasks. Within the framework of investigating diverse neuronal dynamics—integration and resonance—we employ three spiking neuron models to process the original dense word embeddings. The generated sparse temporal codes are then assessed for their capacity to resolve both word-level and sentence-level semantic tasks. The experimental results showcased how our sparse binary word representations delivered performance comparable to or better than original word embeddings in the task of semantic information capture, but with a reduced storage footprint. Language representation, grounded in neuronal activity as demonstrated by our methods, presents a strong foundation potentially applicable to future downstream natural language tasks using neuromorphic systems.
There has been a surge in the research dedicated to low-light image enhancement (LIE) in recent years. The Retinex theory-based deep learning methods, operating through a decomposition-adjustment pipeline, have exhibited impressive performance due to the clear physical meaning embedded within them. Existing deep learning architectures, incorporating Retinex, are not ideal, failing to incorporate the valuable insights from traditional approaches. Concurrently, the adjustment procedure, being either overly simplified or overly complex, demonstrates a lack of practical efficacy. For the purpose of handling these issues, we devise a novel deep learning system targeting LIE. The framework's design includes a decomposition network (DecNet), emulating algorithm unrolling, and integrates adjustment networks that take into account both global and local brightness levels. Algorithm unrolling facilitates the inclusion of implicit priors learned from data and explicit priors from prior methodologies, contributing to a better decomposition. Meanwhile, design of effective yet lightweight adjustment networks is guided by considering global and local brightness. We present a self-supervised fine-tuning strategy, showcasing promising performance without the burden of manually tuning hyperparameters. Our approach's effectiveness, meticulously evaluated against existing state-of-the-art techniques on benchmark LIE datasets, demonstrates its superiority in both quantitative and qualitative performance metrics. The source code for RAUNA2023 is accessible at https://github.com/Xinyil256/RAUNA2023.
The computer vision community has shown considerable interest in supervised person re-identification (ReID) for its substantial real-world applications potential. Nonetheless, the need for human annotation significantly restricts the application's usability due to the prohibitive expense associated with annotating identical pedestrians visible in multiple camera feeds. Ultimately, the pursuit of lowering annotation costs without jeopardizing performance has been the subject of substantial research efforts. Accessories Employing tracklet-informed co-operative annotation, this article outlines a framework to lessen the demand for human input. Robust tracklets are formed by clustering training samples and associating adjacent images within each cluster. This dramatically decreases the annotation workload. Our framework, aiming to lower costs, includes a potent teacher model. This model facilitates active learning, pinpointing the most valuable tracklets for human annotators; the model concurrently serves as an annotator, tagging demonstrably certain tracklets. Ultimately, our final model could attain robust training through a synergy of confident pseudo-labels and human-generated annotations. immune imbalance Experiments performed on three prominent datasets for person re-identification reveal that our approach attains performance competitive with the most advanced methods within active learning and unsupervised learning paradigms.
This study utilizes game theory to analyze the operational strategies of transmitter nanomachines (TNMs) within a three-dimensional (3-D) diffusive channel. Local observations from the specific region of interest (RoI) are relayed to the central supervisor nanomachine (SNM) by transmission nanomachines (TNMs) using information-carrying molecules. All TNMs depend on the common food molecular budget (CFMB) for the creation of information-carrying molecules. By integrating cooperative and greedy strategies, the TNMs aim to obtain their fair portion from the CFMB. In a cooperative arrangement, all TNMs coordinate their communication with the SNM and jointly consume the CFMB, prioritizing group optimization. On the other hand, in a greedy situation, individual TNMs prioritize individual CFMB consumption, aiming for maximum personal gain. The success rate, the error probability, and the receiver operating characteristic (ROC) of RoI detection are used to evaluate the performance. Monte-Carlo and particle-based simulations (PBS) are used for validating the derived results.
Employing a multi-band convolutional neural network (CNN) with band-dependent kernel sizes, we present a novel MI classification method, MBK-CNN, designed to enhance classification accuracy by addressing the subject dependence problem commonly found in CNN-based approaches, which stem from the optimization challenges of kernel sizes. The frequency diversity of EEG signals is exploited in the proposed structure, solving the kernel size problem that differs based on the subject. Overlapping multi-band EEG signal decomposition is achieved, and the resulting signals are routed through multiple CNNs with unique kernel sizes for frequency-specific feature generation. These features are ultimately combined using a weighted summation. In the existing literature, single-band multi-branch CNNs with different kernel sizes are commonly employed to address the subject dependency problem. However, this work proposes utilizing a distinct kernel size for every frequency band. To forestall possible overfitting due to weighted summation, each branch-CNN is trained using a provisional cross-entropy loss; conversely, the integrated network is refined by the end-to-end cross-entropy loss, which is termed amalgamated cross-entropy loss. For enhanced classification performance, we propose a multi-band CNN, MBK-LR-CNN, with enhanced spatial diversity by replacing each branch-CNN with several sub-branch-CNNs that analyze subsets of channels (designated as 'local regions'). We investigated the performance of the MBK-CNN and MBK-LR-CNN methods, using publicly accessible data sources such as the BCI Competition IV dataset 2a and the High Gamma Dataset. The findings of the experiment demonstrate an enhancement in performance for the suggested methodologies, surpassing the capabilities of existing MI classification techniques.
Differential diagnosis of tumors is indispensable for the accuracy of computer-aided diagnosis systems. Computer-aided diagnostic systems frequently face a limitation in expert knowledge regarding lesion segmentation masks, which are primarily utilized during the preprocessing stage or as a supervising mechanism for feature extraction. This study presents a straightforward and highly effective multitask learning network, RS 2-net, to optimize lesion segmentation mask utility. It enhances medical image classification with the help of self-predicted segmentation as a guiding source of knowledge. The RS 2-net process begins with an initial segmentation inference, producing a segmentation probability map. This map is combined with the original image to create a new input, which is reintroduced to the network for the final classification inference.