However, supervised adversarial training has the disadvantage of label leakage, and PGD attack uses cross entropy loss for adversary generation, disregarding the original data manifold structure [19]. These result in the model to overfit on perturbations, thus affecting model generalisation [28], [21...
Adversarial Extreme Multi-label Classification The goal in extreme multi-label classification is to learn a classifier which can assign a small subset of relevant labels to an instance from an extremely large set of target labels. Datasets in extreme classification exhibit a long tail of labels ...
Multi-label classificationAdversarial examples which mislead deep neural networks by adding well-crafted perturbations have become a major threat to classification models. Gradient-based white-box attack algorithms have been widely used to generate adversarial examples. However, most of them are designed ...
a–d, UMAP representation computed from the latent space of MultiVI in which cells are color labeled by their modality (a) and cell-type label (b); scATAC-seq PBMC cells labeled by the replicate from which they were collected (c) and scRNA-seq cells labeled by their experimental technology...
On_Combining_Bags_to_Better_Learn_from_Label_Proportions OpenMSD STraTA aav abps abstract_nas action_angle_networks action_gap_rl activation_clustering active_selective_prediction adaptive_learning_rate_tuner adaptive_prediction adaptive_surrogates adversarial_nets_lr_scheduler aft...
In addition to merging the datasheets, it is important to label each instance as either standard or jammed. In the context of the DSRC dataset, data labeling involves adding a new class called "state" that represents the VENETs connection type (standard or jammed) (see Table 1). Specifically...
respectively. This indicates that adversarial training can enhance prediction performance. The results on other metrics (SN, SP, and AUC) can be found in Additional file (Additional file1: Fig. S4). What is more, to intuitively show the importance of adversarial training in model optimization, ...
Instead, supervised learning methods in multi-omics, which incorporate sample label information, are increasingly applied in disease prognosis and prediction research. ZI-YI YANG et al. [13] proposed the Multi-Modal Self-Paced Learning (MSPL) algorithm for the integration of multi-omics data. ...
Table 6 CICIDS2017 classification label coding table. Full size table Determination of model parameters Some parameters of the convolutional neural network were experimentally tuned and the performance of the model was largely determined by the parameters of the neural network. Firstly, the number of ...
Then, the PBMC cells are annotated by the label transferring method in Seurat V362 with the reference datasets “pbmc_10k_v3.rds” (https://www.dropbox.com/s/zn6khirjafoyyxl/pbmc_10k_v3.rds?dl=0) provided by Satija lab. For the E18 dataset, we transfer the labels from another ...