Md. Ismail Hossain;Mohammed Rakib;M. M. Lutfe Elahi;Nabeel Mohammed;Shafin Rahman
{"title":"COLT: Cyclic Overlapping Lottery Tickets for Faster Pruning of Convolutional Neural Networks","authors":"Md. Ismail Hossain;Mohammed Rakib;M. M. Lutfe Elahi;Nabeel Mohammed;Shafin Rahman","doi":"10.1109/TAI.2025.3534745","DOIUrl":"https://doi.org/10.1109/TAI.2025.3534745","url":null,"abstract":"Pruning refers to the elimination of trivial weights from neural networks. The sub-networks within an overparameterized model produced after pruning are often called lottery tickets. This research aims to generate winning lottery tickets from a set of lottery tickets that can achieve accuracy similar to that of the original unpruned network. We introduce a novel winning ticket called cyclic overlapping lottery ticket (COLT) by data splitting and cyclic retraining of the pruned network from scratch. We apply a cyclic pruning algorithm that keeps only the overlapping weights of different pruned models trained on different data segments. Our results demonstrate that COLT can achieve similar accuracies (obtained by the unpruned model) while maintaining high sparsities. Based on object recognition and detection tasks, we show that the accuracy of COLT is on par with the winning tickets of the lottery ticket hypothesis and, at times, is better. Moreover, COLTs can be generated using fewer iterations than tickets generated by the popular iterative magnitude pruning method. In addition, we also notice that COLTs generated on large datasets can be transferred to small ones without compromising performance, demonstrating its generalizing capability. We conduct all our experiments on Cifar-10, Cifar-100, TinyImageNet, and ImageNet datasets and report superior performance than the state-of-the-art methods. The codes are available at: <uri>https://github.com/ismail31416/COLT</uri>.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 6","pages":"1664-1678"},"PeriodicalIF":0.0,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10855806","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"HWEFIS: A Hybrid Weighted Evolving Fuzzy Inference System for Nonstationary Data Streams","authors":"Tao Zhao;Haoli Li","doi":"10.1109/TAI.2025.3534755","DOIUrl":"https://doi.org/10.1109/TAI.2025.3534755","url":null,"abstract":"For the problem of concept drift of nonstationary data streams, most evolving fuzzy inference systems (EFISs) still encounter problems. First, a single EFIS has difficulty quickly adjusting its own structure and parameters to adapt itself in an environment with obvious dynamic changes (such as sudden drift). Second, most ensemble EFISs adjust their weights according to errors, which is prone to the risk of model undertraining and repeated training. In this article, a new ensemble EFIS, referred to as a hybrid weighted evolving fuzzy inference system (HWEFIS), is proposed. The HWEFIS uses a detection method based on the edge heterogeneous distance (EHD) to mine similarity information between data distributions after data chunks arrive and uses Dempster–Shafer (DS) evidence theory to combine similarity and error information to generate hybrid weights. In addition, a forgetting factor and penalty mechanism are introduced into each base learner, which increases the ability of the base learner to address nonstationary problems. Experiments are carried out on synthetic datasets and real-world datasets. The experimental results show that the HWEFIS can achieve better performance in nonstationary data streams with complex drift, effectively suppresses the influence of concept drift, and is insensitive to the size of the data chunks.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 6","pages":"1679-1694"},"PeriodicalIF":0.0,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"AttDCT: Attention-Based Deep Learning Approach for Time Series Classification in the DCT Domain","authors":"Amine Haboub;Hamza Baali;Abdesselam Bouzerdoum","doi":"10.1109/TAI.2025.3534141","DOIUrl":"https://doi.org/10.1109/TAI.2025.3534141","url":null,"abstract":"This article proposes a new deep learning framework for time series classification in the discrete cosine transform (DCT) domain with spectral enhancement and self-attention mechanisms. The time series signal is first partitioned into discrete segments. Each segment is rearranged into a matrix using a sliding window. The signal matrix is then transformed to spectral coefficients using a two-dimensional (2-D) DCT. This is followed by logarithmic contrast enhancement and spectral normalization to enhance the DCT coefficients. The resulting enhanced coefficient matrix serves as input to a deep neural network architecture comprising a self-attention layer, a multilayer convolutional neural network (CNN), and a fully connected multilayer perceptron (MLP) for classification. The AttDCT CNN model is evaluated and benchmarked on 13 different time series classification problems. The experimental results show that the proposed model outperforms state-of-the-art deep learning methods by an average of 2.1% in classification accuracy. It achieves higher classification accuracy on ten of the problems and similar results on the remaining three.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 6","pages":"1626-1638"},"PeriodicalIF":0.0,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10855682","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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