111 related articles for article (PubMed ID: 38386109)
1. Flipover outperforms dropout in deep learning.
Liang Y; Niu C; Yan P; Wang G
Vis Comput Ind Biomed Art; 2024 Feb; 7(1):4. PubMed ID: 38386109
[TBL] [Abstract][Full Text] [Related]
2. Robustness of Sparsely Distributed Representations to Adversarial Attacks in Deep Neural Networks.
Sardar N; Khan S; Hintze A; Mehra P
Entropy (Basel); 2023 Jun; 25(6):. PubMed ID: 37372277
[TBL] [Abstract][Full Text] [Related]
3. Towards Adversarial Robustness for Multi-Mode Data through Metric Learning.
Khan S; Chen JC; Liao WH; Chen CS
Sensors (Basel); 2023 Jul; 23(13):. PubMed ID: 37448021
[TBL] [Abstract][Full Text] [Related]
4. Regularization of deep neural networks with spectral dropout.
Khan SH; Hayat M; Porikli F
Neural Netw; 2019 Feb; 110():82-90. PubMed ID: 30504041
[TBL] [Abstract][Full Text] [Related]
5. Implicit adversarial data augmentation and robustness with Noise-based Learning.
Panda P; Roy K
Neural Netw; 2021 Sep; 141():120-132. PubMed ID: 33894652
[TBL] [Abstract][Full Text] [Related]
6. Biased Dropout and Crossmap Dropout: Learning towards effective Dropout regularization in convolutional neural network.
Poernomo A; Kang DK
Neural Netw; 2018 Aug; 104():60-67. PubMed ID: 29715684
[TBL] [Abstract][Full Text] [Related]
7. Training Robust Deep Neural Networks via Adversarial Noise Propagation.
Liu A; Liu X; Yu H; Zhang C; Liu Q; Tao D
IEEE Trans Image Process; 2021; 30():5769-5781. PubMed ID: 34161231
[TBL] [Abstract][Full Text] [Related]
8. A regularization perspective based theoretical analysis for adversarial robustness of deep spiking neural networks.
Zhang H; Cheng J; Zhang J; Liu H; Wei Z
Neural Netw; 2023 Aug; 165():164-174. PubMed ID: 37295205
[TBL] [Abstract][Full Text] [Related]
9. Boosting adversarial robustness via self-paced adversarial training.
He L; Ai Q; Yang X; Ren Y; Wang Q; Xu Z
Neural Netw; 2023 Oct; 167():706-714. PubMed ID: 37729786
[TBL] [Abstract][Full Text] [Related]
10. Between-Class Adversarial Training for Improving Adversarial Robustness of Image Classification.
Wang D; Jin W; Wu Y
Sensors (Basel); 2023 Mar; 23(6):. PubMed ID: 36991962
[TBL] [Abstract][Full Text] [Related]
11. Shakeout: A New Approach to Regularized Deep Neural Network Training.
Kang G; Li J; Tao D
IEEE Trans Pattern Anal Mach Intell; 2018 May; 40(5):1245-1258. PubMed ID: 28489533
[TBL] [Abstract][Full Text] [Related]
12. Hybridized sine cosine algorithm with convolutional neural networks dropout regularization application.
Bacanin N; Zivkovic M; Al-Turjman F; Venkatachalam K; Trojovský P; Strumberger I; Bezdan T
Sci Rep; 2022 Apr; 12(1):6302. PubMed ID: 35440609
[TBL] [Abstract][Full Text] [Related]
13. Interpreting and Improving Adversarial Robustness of Deep Neural Networks With Neuron Sensitivity.
Zhang C; Liu A; Liu X; Xu Y; Yu H; Ma Y; Li T
IEEE Trans Image Process; 2021; 30():1291-1304. PubMed ID: 33290221
[TBL] [Abstract][Full Text] [Related]
14. Regularization Meets Enhanced Multi-Stage Fusion Features: Making CNN More Robust against White-Box Adversarial Attacks.
Zhang J; Maeda K; Ogawa T; Haseyama M
Sensors (Basel); 2022 Jul; 22(14):. PubMed ID: 35891112
[TBL] [Abstract][Full Text] [Related]
15. Towards improving fast adversarial training in multi-exit network.
Chen S; Shen H; Wang R; Wang X
Neural Netw; 2022 Jun; 150():1-11. PubMed ID: 35279625
[TBL] [Abstract][Full Text] [Related]
16. Uni-image: Universal image construction for robust neural model.
Ho J; Lee BG; Kang DK
Neural Netw; 2020 Aug; 128():279-287. PubMed ID: 32454372
[TBL] [Abstract][Full Text] [Related]
17. Maximum Relevance Minimum Redundancy Dropout with Informative Kernel Determinantal Point Process.
Saffari M; Khodayar M; Ebrahimi Saadabadi MS; Sequeira AF; Cardoso JS
Sensors (Basel); 2021 Mar; 21(5):. PubMed ID: 33800810
[TBL] [Abstract][Full Text] [Related]
18. Less Is More: Adaptive Trainable Gradient Dropout for Deep Neural Networks.
Avgerinos C; Vretos N; Daras P
Sensors (Basel); 2023 Jan; 23(3):. PubMed ID: 36772365
[TBL] [Abstract][Full Text] [Related]
19. Toward Intrinsic Adversarial Robustness Through Probabilistic Training.
Dong J; Yang L; Wang Y; Xie X; Lai J
IEEE Trans Image Process; 2023; 32():3862-3872. PubMed ID: 37428673
[TBL] [Abstract][Full Text] [Related]
20. Adaptive Dropout Method Based on Biological Principles.
Li H; Weng J; Mao Y; Wang Y; Zhan Y; Cai Q; Gu W
IEEE Trans Neural Netw Learn Syst; 2021 Sep; 32(9):4267-4276. PubMed ID: 33872159
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
