Experiments on the CIFAR10 dataset showed that using only $10\%$ of the full training set, the proposed method was able to adequately defend the model against the AutoPGD attack while maintaining a classification accuracy on clean images outperforming the model with adversarial training by $7\%$.
Second, we design an iterative self-training strategy to align the fine-grained class distributions for the source and target domains via target domain pseudo labels.
We propose an audio-visual spatial-temporal deep neural network with: (1) a visual block containing a pretrained 2D-CNN followed by a temporal convolutional network (TCN); (2) an aural block containing several parallel TCNs; and (3) a leader-follower attentive fusion block combining the audio-visual information.
In this paper, we propose an unsupervised Time-Series representation learning framework via Temporal and Contextual Contrasting (TS-TCC), to learn time-series representation from unlabeled data.
Ranked #1 on Recognizing And Localizing Human Actions on HAR
Deep learning has achieved promising segmentation performance on 3D left atrium MR images.
In this paper, we propose LGG, a neurologically inspired graph neural network, to learn local-global-graph representations from Electroencephalography (EEG) for a Brain-Computer Interface (BCI).
The MRCNN can extract low and high frequency features and the AFR is able to improve the quality of the extracted features by modeling the inter-dependencies between the features.
Ranked #1 on Automatic Sleep Stage Classification on Sleep-EDF
TSception consists of dynamic temporal, asymmetric spatial, and high-level fusion layers, which learn discriminative representations in the time and channel dimensions simultaneously.
With this design, we compare FBCNet with state-of-the-art (SOTA) BCI algorithm on four MI datasets: The BCI competition IV dataset 2a (BCIC-IV-2a), the OpenBMI dataset, and two large datasets from chronic stroke patients.
no code implementations • 7 Feb 2021 • Phairot Autthasan, Rattanaphon Chaisaen, Thapanun Sudhawiyangkul, Phurin Rangpong, Suktipol Kiatthaveephong, Nat Dilokthanakul, Gun Bhakdisongkhram, Huy Phan, Cuntai Guan, Theerawit Wilaiprasitporn
We integrate deep metric learning into a multi-task autoencoder to learn a compact and discriminative latent representation from EEG and perform classification simultaneously.
We use the proposed criteria to select samples for strong and weak labelers to produce oracle labels and pseudo labels simultaneously at each active learning iteration in an ensemble learning manner, which can be examined with IoMT Platform.
Our model is able to perform on a calibration-free user-independent scenario, which is desirable for clinical diagnostics.
Ranked #1 on EEG on HS-SSVEP
The success of deep learning (DL) methods in the Brain-Computer Interfaces (BCI) field for classification of electroencephalographic (EEG) recordings has been restricted by the lack of large datasets.
TSception consists of temporal and spatial convolutional layers, which learn discriminative representations in the time and channel domains simultaneously.
Convolutional Neural Network (CNN) has been successfully applied on classification of both natural images and medical images but not yet been applied to differentiating patients with schizophrenia from healthy controls.
We observed that CSTA achieves the maximum mean accuracy of 97. 43$\pm$2. 26 % and 85. 71$\pm$13. 41 % with four-class and forty-class SSVEP data-sets respectively in sub-second response time in offline analysis.
We implement a visual interpretability method Layer-wise Relevance Propagation (LRP) on top of 3D U-Net trained to perform lesion segmentation on the small dataset of multi-modal images provided by ISLES 2017 competition.
no code implementations • 10 Oct 2019 • Manzhang Xu, Bijun Tang, Yuhao Lu, Chao Zhu, Lu Zheng, Jingyu Zhang, Nannan Han, Yuxi Guo, Jun Di, Pin Song, Yongmin He, Lixing Kang, Zhiyong Zhang, Wu Zhao, Cuntai Guan, Xuewen Wang, Zheng Liu
Reducing the lateral scale of two-dimensional (2D) materials to one-dimensional (1D) has attracted substantial research interest not only to achieve competitive electronic device applications but also for the exploration of fundamental physical properties.
Unfortunately, the blackbox nature of the deep learning is still unresolved, and many machine decisions are still poorly understood.
Synthesis of advanced inorganic materials with minimum number of trials is of paramount importance towards the acceleration of inorganic materials development.