Knowledge-Based Systems最新文献_第5页

TreeC: A method to generate interpretable energy management systems using a metaheuristic algorithm

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-11-20 DOI: 10.1016/j.knosys.2024.112756

Julian Ruddick , Luis Ramirez Camargo , Muhammad Andy Putratama , Maarten Messagie , Thierry Coosemans

{"title":"TreeC: A method to generate interpretable energy management systems using a metaheuristic algorithm","authors":"Julian Ruddick , Luis Ramirez Camargo , Muhammad Andy Putratama , Maarten Messagie , Thierry Coosemans","doi":"10.1016/j.knosys.2024.112756","DOIUrl":"10.1016/j.knosys.2024.112756","url":null,"abstract":"<div><div>Energy management systems (EMS) have traditionally been implemented using rule-based control (RBC) and model predictive control (MPC) methods. However, recent research has explored the use of reinforcement learning (RL) as a promising alternative. This paper introduces TreeC, a machine learning method that utilises the covariance matrix adaptation evolution strategy metaheuristic algorithm to generate an interpretable EMS modelled as a decision tree. Unlike RBC and MPC approaches, TreeC learns the decision strategy of the EMS based on historical data, adapting the control model to the controlled energy grid. The decision strategy is represented as a decision tree, providing interpretability compared to RL methods that often rely on black-box models like neural networks. TreeC is evaluated against MPC with perfect forecast and RL EMSs in two case studies taken from literature: an electric grid case and a household heating case. In the electric grid case, TreeC achieves an average energy loss and constraint violation score of 19.2, which is close to MPC and RL EMSs that achieve scores of 14.4 and 16.2 respectively. All three methods control the electric grid well especially when compared to the random EMS, which obtains an average score of 12875. In the household heating case, TreeC performs similarly to MPC on the adjusted and averaged electricity cost and total discomfort (0.033 EUR/m<sup>2</sup> and 0.42 Kh for TreeC compared to 0.037 EUR/m<sup>2</sup> and 2.91 kH for MPC), while outperforming RL (0.266 EUR/m<sup>2</sup> and 24.41 Kh). TreeC demonstrates a performant and interpretable application of machine learning for EMSs.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"309 ","pages":"Article 112756"},"PeriodicalIF":7.2,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142748702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Diverse Semantic Image Synthesis with various conditioning modalities

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-11-19 DOI: 10.1016/j.knosys.2024.112727

Chaoyue Wu , Rui Li , Cheng Liu , Si Wu , Hau-San Wong

{"title":"Diverse Semantic Image Synthesis with various conditioning modalities","authors":"Chaoyue Wu , Rui Li , Cheng Liu , Si Wu , Hau-San Wong","doi":"10.1016/j.knosys.2024.112727","DOIUrl":"10.1016/j.knosys.2024.112727","url":null,"abstract":"<div><div>Semantic image synthesis aims to generate high-fidelity images from a segmentation mask, and previous methods typically train a generator to associate a global random map with the conditioning mask. However, the lack of independent control of regional content impedes their application. To address this issue, we propose an effective approach for Multi-modal conditioning-based Diverse Semantic Image Synthesis, which is referred to as McDSIS. In this model, there are a number of constituent generators incorporated to synthesize the content in semantic regions from independent random maps. The regional content can be determined by the style code associated with a random map, extracted from a reference image, or by embedding a textual description via our proposed conditioning mechanisms. As a result, the generation process is spatially disentangled, which facilitates independent synthesis of diverse content in a semantic region, while at the same time preserving other content. Due to this flexible architecture, in addition to achieving superior performance over state-of-the-art semantic image generation models, McDSIS is capable of performing various visual tasks, such as face inpainting, swapping, local editing, etc.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"309 ","pages":"Article 112727"},"PeriodicalIF":7.2,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142748706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep spectral clustering by integrating local structure and prior information 通过整合局部结构和先验信息进行深度光谱聚类

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-11-19 DOI: 10.1016/j.knosys.2024.112743

Hua Meng , Yueyi Zhang , Zhiguo Long

{"title":"Deep spectral clustering by integrating local structure and prior information","authors":"Hua Meng , Yueyi Zhang , Zhiguo Long","doi":"10.1016/j.knosys.2024.112743","DOIUrl":"10.1016/j.knosys.2024.112743","url":null,"abstract":"<div><div>The traditional spectral clustering (SC) is an effective clustering method that can handle data with complex structure. SC essentially embeds data in another feature space with time-consuming spectral embedding before clustering, and has to re-embed the whole data when unseen data arrive, lacking the so-called <em>out-of-sample-extension</em> capability. SpectralNet (Shaham et al., 2018) is a pioneer attempt to resolve these two problems by training with random mini-batches to scale to large-scale data and by an orthogonal transformation layer to ensure orthogonality of embeddings and remove redundancy in features. However, the randomly selected data in each mini-batch might be far away from each other and fail to convey local structural information; the orthogonal transformation can only ensure orthogonality for each mini-batch instead of the whole data. In this paper, we propose a novel approach to address these two problems. By improving data selection for batches with <em>batch augmentation</em> using neighboring information, it helps the network to better capture local structural information. By devising <em>core point guidance</em> to exploit the spectral embeddings of representative points as prior information, it guides the network to learn embeddings that can better maintain the overall structures of data points. Empirical results show that our method resolves the two problems of SpectralNet and exhibits superior clustering performance to SpectralNet and other state-of-the-art deep clustering algorithms, while being able to generalize the embedding to unseen data.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"308 ","pages":"Article 112743"},"PeriodicalIF":7.2,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142720587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

EGFDA: Experience-guided Fine-grained Domain Adaptation for cross-domain pneumonia diagnosis EGFDA：经验指导下的细粒度领域适应性，用于跨领域肺炎诊断

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-11-19 DOI: 10.1016/j.knosys.2024.112752

Haoran Zhao , Tao Ren , Wei Li , Danke Wu , Zhe Xu

{"title":"EGFDA: Experience-guided Fine-grained Domain Adaptation for cross-domain pneumonia diagnosis","authors":"Haoran Zhao , Tao Ren , Wei Li , Danke Wu , Zhe Xu","doi":"10.1016/j.knosys.2024.112752","DOIUrl":"10.1016/j.knosys.2024.112752","url":null,"abstract":"<div><div>Although recent advances in deep learning have led to accurate pneumonia diagnoses, their heavy reliance on data annotation hinders their expected performance in clinical practice. Unsupervised domain adaptation (UDA) methods have been developed to address the scarcity of annotations. Nevertheless, the diverse manifestations of pneumonia pose challenges for current UDA methods, including spatial lesion-preference bias and discriminative class-preference bias. To overcome these problems, we propose an Experience-Guided Fine-grained Domain Adaptation (EGFDA) framework for automatic cross-domain pneumonia diagnosis. Our framework consists of two main modules: (1) Gradient-aware Lesion Area Matching (GaLAM), which aims to reduce the global domain gap while avoiding misleading from lesion-unrelated targets, and (2) Reweighing Smooth Certainty-aware Matching (RSCaM), which aims to match class space with a smooth certainty-aware feature mapping to guide the model to learn more precise class-discriminative features. Benefiting from the collaboration between GaLAM and RSCaM, the proposed EGFDA is able to process unlabeled samples following a pattern similar to the diagnostic experience of physicians, that is, first locating the disease-related lesion area and then performing fine-grained discrimination. Comprehensive experiments on three different tasks using six datasets demonstrate the superior performance of our EGFDA. Furthermore, extensive ablation studies and visual analyses highlight the remarkable interpretability and generalization of the proposed method.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"307 ","pages":"Article 112752"},"PeriodicalIF":7.2,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142698124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Contrastive Predictive Embedding for learning and inference in knowledge graph 用于知识图谱学习和推理的对比预测嵌入法

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-11-19 DOI: 10.1016/j.knosys.2024.112730

Chen Liu, Zihan Wei, Lixin Zhou

{"title":"Contrastive Predictive Embedding for learning and inference in knowledge graph","authors":"Chen Liu, Zihan Wei, Lixin Zhou","doi":"10.1016/j.knosys.2024.112730","DOIUrl":"10.1016/j.knosys.2024.112730","url":null,"abstract":"<div><div>Knowledge graph embedding (KGE) aims to capture rich semantic information about entities and relationships in KGs, which is essential for Knowledge Graph Completion (KGC) and various downstream tasks. Existing KGE models differentiate between entity and relationship embeddings by constructing indirect pretext tasks and scoring functions to discern different types of triplets. In contrast, this paper introduces a novel KGE method called Contrastive Predictive Embedding (CPE), which dispenses with the need for defining scoring functions or negative sampling. Specifically, CPE directly predicts embeddings for unknown entities based on the known entity and relationship embeddings in triplets and compares them with the true embeddings. Additionally, this paper proposes a special optimization approach to enhance the performance of various Translation-based models. Experimental results on four benchmark KGs demonstrate that CPE improves the performance of original KGE models while maintaining lower computational complexity. On the FB15k-237 dataset, CPE enhances the MRR and <span><math><mrow><mtext>Hit</mtext><mi>@</mi><mi>k</mi><mrow><mo>(</mo><mi>k</mi><mo>∈</mo><mrow><mo>{</mo><mn>1</mn><mo>,</mo><mn>3</mn><mo>,</mo><mn>10</mn><mo>}</mo></mrow><mo>)</mo></mrow></mrow></math></span> metrics of TransE by 1.55%, 3.37%, 4.58%, and 5.92%, respectively.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"307 ","pages":"Article 112730"},"PeriodicalIF":7.2,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142698126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive Cross-Modal Experts Network with Uncertainty-Driven Fusion for Vision–Language Navigation 用于视觉语言导航的不确定性驱动融合的自适应跨模态专家网络

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-11-19 DOI: 10.1016/j.knosys.2024.112735

Jie Wu , Chunlei Wu , Xiuxuan Shen , Leiquan Wang

{"title":"Adaptive Cross-Modal Experts Network with Uncertainty-Driven Fusion for Vision–Language Navigation","authors":"Jie Wu , Chunlei Wu , Xiuxuan Shen , Leiquan Wang","doi":"10.1016/j.knosys.2024.112735","DOIUrl":"10.1016/j.knosys.2024.112735","url":null,"abstract":"<div><div>Vision-and-Language Navigation (VLN) enables an agent to autonomously navigate in real-world environments based on language instructions to reach specified destinations and accurately locate relevant targets. Although significant progress has been made in recent years, two major limitations remain: (1) Existing methods lack flexibility and diversity in processing multimodal information and cannot dynamically adjust to different input features. (2) Current fixed fusion strategies fail to dynamically adapt to varying data quality in open environments, insufficiently leveraging multi-scale features and handling complex nonlinear relationships. In this paper, an adaptive cross-modal experts network (ACME) with uncertainty-driven fusion is proposed to address these issues. The adaptive cross-modal experts module dynamically selects the most suitable expert network based on the input features, enhancing information processing diversity and flexibility. Additionally, the uncertainty-driven fusion module balances coarse-grained and fine-grained information by calculating their confidences and dynamically adjusting the fusion weights. Comprehensive experiments on the R2R, SOON, and REVERIE datasets demonstrate that our approach significantly outperforms existing VLN approaches.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"307 ","pages":"Article 112735"},"PeriodicalIF":7.2,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142698122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A lightweight convolutional neural network for road surface classification under shadow interference 用于阴影干扰下路面分类的轻量级卷积神经网络

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-11-17 DOI: 10.1016/j.knosys.2024.112761

Ruichi Mao, Guangqiang Wu, Jian Wu, Xingyu Wang

{"title":"A lightweight convolutional neural network for road surface classification under shadow interference","authors":"Ruichi Mao, Guangqiang Wu, Jian Wu, Xingyu Wang","doi":"10.1016/j.knosys.2024.112761","DOIUrl":"10.1016/j.knosys.2024.112761","url":null,"abstract":"<div><div>The development of intelligent driving, especially in the intelligent control of active suspension, heavily relies on the predictive perception of upcoming road conditions. To achieve accurate real-time road surface classification and overcome shadow interference, a lightweight convolutional neural network (CNN) based on a novel data augmentation method is proposed and an improved cycle-consistent adversarial network (CycleGAN) is developed to generate shadowed pavement data. The CycleGAN network structure is optimized using the texture self-supervised (TSS) mechanism and the learned perceptual image patch similarity (LPIPS) function, with label smoothing applied during training. The images produced by this data augmentation method closely resemble real-world images. Furthermore, Efficient-MBConv, which offers the advantages of fewer parameters and higher precision, is proposed. Finally, the Light-EfficientNet architecture, based on Efficient-MBConv, is developed and trained on the augmented dataset. Compared with EfficientNet-B0, the number of parameters in Light-EfficientNet is reduced by 61.94 %. The Light-EfficientNet model trained with data augmentation demonstrates an average classification accuracy improvement of 5.76 % on the test set with shadows, compared with the model trained without data augmentation. This approach effectively reduces the impact of shadows on road classification at a lower cost, while also significantly reducing the computational resources required by the CNN, providing real-time and accurate road surface information for the control of active suspension height and damping.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"306 ","pages":"Article 112761"},"PeriodicalIF":7.2,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

QA-TSN: QuickAccurate Tongue Segmentation Net QA-TSN：快速准确舌苔分割网

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-11-15 DOI: 10.1016/j.knosys.2024.112648

Guangze Jia, Zhenchao Cui, Qingsong Fei

{"title":"QA-TSN: QuickAccurate Tongue Segmentation Net","authors":"Guangze Jia, Zhenchao Cui, Qingsong Fei","doi":"10.1016/j.knosys.2024.112648","DOIUrl":"10.1016/j.knosys.2024.112648","url":null,"abstract":"<div><div>Tongue segmentation is an essential part for computer-aided tongue diagnosis. Since of similar color and texture between tongue body and non-tongue body, such as lips and face, existing methods produce the lack of accuracy and completeness for tongue segmentation results. Moreover, small samples in tongue datasets lead under-fitting on CNN-based methods which always produce poor segmentation. To solve these problems, we designed the quick accurate tongue segmentation net (QA-TSN) to segment tongue body. To alleviate small sample problem, in the proposed method, a tongue-style transfer generation net(T-STGN) was propose to synthesize tongue images. In T-STGN, a novel encoder–decoder structure with two encoder with a global rendering block was used to refine global characteristics of synthetic tongue images. For real-time tongue segmentation, quicker tongue segmentation net (QTSN) was proposed in QA-TSN. In QTSN, we used an encoder–decoder structure with modified partial convolution (MPConv) to expedite the computation for real-time segmentation. To smooth the segments of tongue body, a novel loss function of tongue segmentation loss (TSL) was proposed. In TSL, tongue edge loss (TEL) was used to smooth the boundary of segmentation of tongue body and tongue area loss (TAL) was proposed to improve the fragmentation of segmentation results. Experiments conducted on tongue datasets achieved an IoU of 98.0307 and a Dice score of 99.0738, with a frame rate of 75.35, outperforming all other methods involved in the experiment. These results demonstrate the effectiveness of the proposed QA-TSN.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"307 ","pages":"Article 112648"},"PeriodicalIF":7.2,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142698123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A confidence-based knowledge integration framework for cross-domain table question answering 基于置信度的跨域表格问题解答知识整合框架

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-11-15 DOI: 10.1016/j.knosys.2024.112718

Yuankai Fan , Tonghui Ren , Can Huang , Beini Zheng , Yinan Jing , Zhenying He , Jinbao Li , Jianxin Li

{"title":"A confidence-based knowledge integration framework for cross-domain table question answering","authors":"Yuankai Fan , Tonghui Ren , Can Huang , Beini Zheng , Yinan Jing , Zhenying He , Jinbao Li , Jianxin Li","doi":"10.1016/j.knosys.2024.112718","DOIUrl":"10.1016/j.knosys.2024.112718","url":null,"abstract":"<div><div>Recent advancements in TableQA leverage sequence-to-sequence (Seq2seq) deep learning models to accurately respond to natural language queries. These models achieve this by converting the queries into SQL queries, using information drawn from one or more tables. However, Seq2seq models often produce uncertain (low-confidence) predictions when distributing probability mass across multiple outputs during a decoding step, frequently yielding translation errors. To tackle this problem, we present <span>Ckif</span>, a <em>confidence-based knowledge integration framework</em> that uses a two-stage deep-learning-based ranking technique to mitigate the low-confidence problem commonly associated with Seq2seq models for TableQA. The core idea of <span>Ckif</span> is to introduce a flexible framework that seamlessly integrates with any existing Seq2seq translation models to enhance their performance. Specifically, by inspecting the probability values in each decoding step, <span>Ckif</span> first masks out each low-confidence prediction from the predicted outcome of an underlying Seq2seq model. Subsequently, <span>Ckif</span> integrates prior knowledge of query language to generalize masked-out queries, enabling the generation of all possible queries and their corresponding NL expressions. Finally, a two-stage deep-learning ranking approach is developed to evaluate the semantic similarity of NL expressions to a given NL question, hence determining the best-matching result. Extensive experiments are conducted to investigate <span>Ckif</span> by applying it to five state-of-the-art Seq2seq models using a widely used public benchmark. The experimental results indicate that <span>Ckif</span> consistently enhances the performance of all the Seq2seq models, demonstrating its effectiveness for better supporting TableQA.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"306 ","pages":"Article 112718"},"PeriodicalIF":7.2,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing accuracy of compressed Convolutional Neural Networks through a transfer teacher and reinforcement guided training curriculum 通过转移教师和强化引导训练课程提高压缩卷积神经网络的准确性

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-11-15 DOI: 10.1016/j.knosys.2024.112719

Anusha Jayasimhan, Pabitha P.

{"title":"Enhancing accuracy of compressed Convolutional Neural Networks through a transfer teacher and reinforcement guided training curriculum","authors":"Anusha Jayasimhan, Pabitha P.","doi":"10.1016/j.knosys.2024.112719","DOIUrl":"10.1016/j.knosys.2024.112719","url":null,"abstract":"<div><div>Model compression techniques, such as network pruning, quantization and knowledge distillation, are essential for deploying large Convolutional Neural Networks (CNNs) on resource-constrained devices. Nevertheless, these techniques frequently lead to an accuracy loss, which affects performance in applications where precision is crucial. To mitigate accuracy loss, a novel method integrating Curriculum Learning (CL) with model compression, is proposed. Curriculum learning is a training approach in machine learning that involves progressively training a model on increasingly difficult samples. Existing CL approaches primarily rely on the manual design of scoring the difficulty of samples as well as pacing the easy to difficult examples for training. This gives rise to limitations such as inflexibility, need for expert domain knowledge and a decline in performance. Thereby, we propose a novel curriculum learning approach TRACE-CNN, i.e <strong><u>T</u></strong>ransfer-teacher and <strong><u>R</u></strong>einforcement-guided <strong><u>A</u></strong>daptive <strong><u>C</u></strong>urriculum for <strong><u>E</u></strong>nhancing <strong><u>C</u></strong>onvolutional <strong><u>N</u></strong>eural <strong><u>N</u></strong>etworks, to address these limitations. Our semi-automated CL method consists of a pre-trained transfer teacher model whose performance serves as a measure of difficulty for the training examples. Furthermore, we employ a reinforcement learning technique to schedule training according to sample difficulty rather than establishing a fixed scheduler. Experiments on two benchmark datasets demonstrate that our method, when integrated into a model compression pipeline, effectively reduces the accuracy loss usually associated with such compression techniques.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"306 ","pages":"Article 112719"},"PeriodicalIF":7.2,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0