Xception: Deep Learning with Depthwise Separable Convolutions. We present an interpretation of Inception modules in convolutional neural networks as being an intermediate step in-between regular convolution and the depthwise separable convolution operation (a depthwise convolution followed by a pointwise convolution). In this light, a depthwise separable convolution can be understood as an Inception module with a maximally large number of towers. This observation leads us to propose a novel deep convolutional neural network architecture inspired by Inception, where Inception modules have been replaced with depthwise separable convolutions. We show that this architecture, dubbed Xception, slightly outperforms Inception V3 on the ImageNet dataset (which Inception V3 was designed for), and significantly outperforms Inception V3 on a larger image classification dataset comprising 350 million images and 17,000 classes. Since the Xception architecture has the same number of parameters as Inception V3, the performance gains are not due to increased capacity but rather to a more efficient use of model parameters.

References in zbMATH (referenced in 23 articles )

Showing results 1 to 20 of 23.
Sorted by year (citations)

1 2 next

  1. Chandra, N. V. Megha; Reddy, K. Ashish; Sushanth, G.; Sujatha, S.: A versatile approach based on convolutional neural networks for early identification of diseases in tomato plants (2022)
  2. Liu, Xiaoguang; Chen, Meng; Liang, Tie; Lou, Cunguang; Wang, Hongrui; Liu, Xiuling: A lightweight double-channel depthwise separable convolutional neural network for multimodal fusion gait recognition (2022)
  3. Panigrahi, Santisudha; Bhuyan, Ruchi; Kumar, Kundan; Nayak, Janmenjoy; Swarnkar, Tripti: Multistage classification of oral histopathological images using improved residual network (2022)
  4. Roslidar, Roslidar; Syaryadhi, Mohd; Saddami, Khairun; Pradhan, Biswajeet; Arnia, Fitri; Syukri, Maimun; Munadi, Khairul: BreaCNet: a high-accuracy breast thermogram classifier based on mobile convolutional neural network (2022)
  5. Wang, Hengjie; Planas, Robert; Chandramowlishwaran, Aparna; Bostanabad, Ramin: Mosaic flows: a transferable deep learning framework for solving PDEs on unseen domains (2022)
  6. Liu, Chunlei; Ding, Wenrui; Hu, Yuan; Zhang, Baochang; Liu, Jianzhuang; Guo, Guodong; Doermann, David: Rectified binary convolutional networks with generative adversarial learning (2021)
  7. Mark Weber, Huiyu Wang, Siyuan Qiao, Jun Xie, Maxwell D. Collins, Yukun Zhu, Liangzhe Yuan, Dahun Kim, Qihang Yu, Daniel Cremers, Laura Leal-Taixe, Alan L. Yuille, Florian Schroff, Hartwig Adam, Liang-Chieh Chen: DeepLab2: A TensorFlow Library for Deep Labeling (2021) arXiv
  8. Peng, Jianzhong; Zhu, Wei; Liang, Qiaokang; Li, Zhengwei; Lu, Maoying; Sun, Wei; Wang, Yaonan: Defect detection in code characters with complex backgrounds based on BBE (2021)
  9. Rajpal, Sheetal; Lakhyani, Navin; Singh, Ayush Kumar; Kohli, Rishav; Kumar, Naveen: Using handpicked features in conjunction with resnet-50 for improved detection of COVID-19 from chest X-ray images (2021)
  10. Wang, Ruhua; An, Senjian; Liu, Wanquan; Li, Ling: Fixed-point algorithms for inverse of residual rectifier neural networks (2021)
  11. Wang, Zhengyang; Ji, Shuiwang: Smoothed dilated convolutions for improved dense prediction (2021)
  12. Chen, Yiming; Pan, Tianci; He, Cheng; Cheng, Ran: Efficient evolutionary deep neural architecture search (NAS) by noisy network morphism mutation (2020)
  13. Kumar, Kamlesh; Saeed, Umair; Rai, Athaul; Islam, Noman; Shaikh, Ghulam Muhammad; Qayoom, Abdul: IDC breast cancer detection using deep learning schemes (2020)
  14. Liu, Li; Ouyang, Wanli; Wang, Xiaogang; Fieguth, Paul; Chen, Jie; Liu, Xinwang; Pietikäinen, Matti: Deep learning for generic object detection: a survey (2020)
  15. Sharma, Vipul; Mir, Roohie Naaz: A comprehensive and systematic look up into deep learning based object detection techniques: a review (2020)
  16. Tan, Hao; He, Cheng; Tang, Dexuan; Cheng, Ran: Efficient evolutionary neural architecture search (NAS) by modular inheritable crossover (2020)
  17. Valada, Abhinav; Mohan, Rohit; Burgard, Wolfram: Self-supervised model adaptation for multimodal semantic segmentation (2020)
  18. Zheng, Qinghe; Tian, Xinyu; Yang, Mingqiang; Wu, Yulin; Su, Huake: PAC-Bayesian framework based drop-path method for 2D discriminative convolutional network pruning (2020)
  19. Ahsen, Mehmet Eren; Vogel, Robert M.; Stolovitzky, Gustavo A.: Unsupervised evaluation and weighted aggregation of ranked classification predictions (2019)
  20. Huan, Er-Yang; Wen, Gui-Hua: Multilevel and multiscale feature aggregation in deep networks for facial constitution classification (2019)

1 2 next