Search Results - Classifier Discriminative Autoencoder

Comparative Analysis of Unsupervised and Supervised Autoencoders for Nuclei Classification in Clear Cell Renal Cell Carcinoma Images by Javadian, Fatemeh, Aminparast, Zahra, Stegmaier, Johannes, Jose, Abin

“…), as well as a classifier-based discriminative AE (CDAE), optimized using the hyperparameter tuning tool Optuna…”
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Discriminative ensemble learning for few-shot chest x-ray diagnosis by Paul, Angshuman, Tang, Yu-Xing, Shen, Thomas C., Summers, Ronald M.

“…•The saliency-based classifier is designed using a novel discriminative autoencoder ensemble.•We introduce a novel intrinsic weight to be assigned to each autoencoder for weighted voting during inference…”
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Learning Compact and Discriminative Stacked Autoencoder for Hyperspectral Image Classification by Zhou, Peicheng, Han, Junwei, Cheng, Gong, Zhang, Baochang

“… To address this issue, this paper proposes an effective framework, named compact and discriminative stacked autoencoder (CDSAE…”
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Deep Manifold Preserving Autoencoder for Classifying Breast Cancer Histopathological Images by Feng, Yangqin, Zhang, Lei, Mo, Juan

“…, the tissue preparation, and staining processes. In this paper, we propose a new feature extractor, called deep manifold preserving autoencoder, to learn discriminative features from unlabeled data…”
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AE-LGBM: Sequence-based novel approach to detect interacting protein pairs via ensemble of autoencoder and LightGBM by Sharma, Abhibhav, Singh, Buddha

“… It employs LightGBM classifier and utilizes…”
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Breath analysis based early gastric cancer classification from deep stacked sparse autoencoder neural network by Aslam, Muhammad Aqeel, Xue, Cuili, Chen, Yunsheng, Zhang, Amin, Liu, Manhua, Wang, Kan, Cui, Daxiang

“… In this study, we proposed a new method for feature extraction using a stacked sparse autoencoder to extract the discriminative features from the unlabeled data of breath samples…”
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Discriminative Stacked Autoencoder: Feature-Integration Boosting for Bearing Fault Diagnosis by Liu, Shiya, He, Jun, Chen, Zhiwen, Chen, Danfeng, Chen, Yong

“… To address this problem, we propose a feature-integration-boosting-based discriminative stacked autoencoder (D-SAE…”
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Few-Shot Network Intrusion Detection Using Discriminative Representation Learning with Supervised Autoencoder by Iliyasu, Auwal Sani, Abdurrahman, Usman Alhaji, Zheng, Lirong

“…Recently, intrusion detection methods based on supervised deep learning techniques (DL) have seen widespread adoption by the research community, as a result of…”
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FVAE: a regularized variational autoencoder using the Fisher criterion by Lai, Jie, Wang, Xiaodan, Xiang, Qian, Li, Rui, Song, Yafei

“… To obtain discriminative latent variables and generated samples, a Fisher variational autoencoder (FVAE…”
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Discriminative Autoencoders for Small Targets Detection by Razakarivony, Sebastien, Jurie, Frederic

“…This paper introduces the new concept of discriminative auto encoders. In contrast with the standard auto encoders - which are artificial neural networks used…”
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SERS-based sensor with a machine learning based effective feature extraction technique for fast detection of colistin-resistant Klebsiella pneumoniae by Ciloglu, Fatma Uysal, Hora, Mehmet, Gundogdu, Aycan, Kahraman, Mehmet, Tokmakci, Mahmut, Aydin, Omer

“…) have shown some spectral differences that hard to discriminate by the naked eye. To extract discriminative features from the dataset, autoencoder and principal component analysis (PCA…”
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Robustifying models against adversarial attacks by Langevin dynamics by Srinivasan, Vignesh, Rohrer, Csaba, Marban, Arturo, Müller, Klaus-Robert, Samek, Wojciech, Nakajima, Shinichi

“…) in alignment with a discriminative classifier. Our algorithm, called MALA for DEfense (MALADE…”
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Few-Shot Learning with Discriminative Representation for Cyberattack Detection by Cao, Van Loi, Nguyen, Manh Tuan, Le Dinh, Trang Dang

“…attacks to few-shot learning-based classifiers for identifying novel/rare anomaly categories. However, the Discriminative AutoEncoder is trained to prefer representing normal data than anomalies without any constrains on its latent feature space…”
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Deep Discriminative Representation Learning for Nonlinear Process Fault Detection by Jiang, Qingchao, Yan, Xuefeng, Huang, Biao

“… By using the training data from the normal operation training data and the constructed validation data, a DNN with stacked autoencoders and a softmax classifier is trained to generate discriminative…”
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Personal Identification Using 3D Topographic Cubes Extracted from EEG Signals by Means of Automated Feature Representation by Oztemel, Muhammed Esad, Soysal, Ömer Muhammet

“… This study proposes a framework based on a convolutional autoencoder (CAE) along with a traditional classifier for identifying individuals using EEG brainprints…”
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Comparative analysis of autoencoder architectures for breast cancer detection using dynamic infrared thermography by Acar Demirci, Burcu, Engin, Mehmet, Engin, Erkan Zeki

“…), which captures surface temperature variations that may indicate malignancy. This study proposes a novel ITI-based diagnostic framework integrating deep learning-driven feature extraction with conventional machine learning classifiers…”
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Discriminative Mixture Variational Autoencoder for Semisupervised Classification by Chen, Jian, Du, Lan, Liao, Leiyao

“…In this article, a deep probability model, called the discriminative mixture variational autoencoder (DMVAE…”
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Joint optimization of autoencoder and Self-Supervised Classifier: Anomaly detection of strawberries using hyperspectral imaging by Liu, Yisen, Zhou, Songbin, Wu, Hongmin, Han, Wei, Li, Chang, Chen, Hong

“…[Display omitted] •Detecting strawberry anomalies using hyperspectral imaging.•Constructing models by joint optimization of autoencoder and self-supervised learning…”
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Sequential Variational Autoencoder with Adversarial Classifier for Video Disentanglement by Haga, Takeshi, Kera, Hiroshi, Kawamoto, Kazuhiko

“… Additionally, we found that dynamic features are not discriminative in the latent space. To address these problems, we introduced an adversarial classifier using supervised learning into the two-stream architecture…”
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Discriminative Autoencoder by Paul, Angshuman, Majumdar, Angshul, Mukherjee, Dipti Prasad

“…) is important for many classification problems especially in biomedical imaging. We propose a discriminative autoencoder, useful for addressing the challenge of classification using cross-datasets…”
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