But these methods are unable to improve throughput (frames-per-second) on real-life hardware while simultaneously preserving robustness toadversarial perturbations.

Sparse training has received an upsurging interest in machine learning due to its tantalizing saving potential for the entire training process as well as inference.

We propose a Level Set Traversal algorithm that iteratively explores regions of high confidence with respect to the input space using orthogonal components of the local gradients.

Therefore, from a system-level perspective, the design ethos of a system-efficient communication-compression algorithm is that we should guarantee that the compression/decompression of the algorithm is computationally light and takes less time, and it should also be friendly to efficient collective communication primitives.

Since deep neural networks (DNNs) became successful, domain adaptation methods also leveraged them for representation learning.

Table 1 summarizes the models and datasets used in this work. ImageNet-1K Deng u. a. (2009): We use the subset of the ImageNet dataset containing DeepCAM Kurth u. a. (2018): DeepCAM dataset for image segmentation, which consists of Fractal-3K Kataoka u. a. (2022) A rendered dataset from the Visual Atom method Kataoka We also use the setting in Kataoka u. a. (2022) Table 2 shows the detail of our hyper-parameters. Specifically, We follow the guideline of'TorchVision' to train the ResNet-50 that uses the CosineLR To show the robustness of KAKURENBO, we also train ResNet-50 with different settings, e.g., ResNet-50 (A) setting, we follow the hyper-parameters reported in Goyal u. a. (2017). It is worth noting that KAKURENBO merely hides samples before the input pipeline. In this section, we present an analysis of the factors affecting KAKURENBO's performance, e.g., the The result shows that our method could dynamically hide the samples at each epoch.

This paper proposes a method for hiding the least-important samples during the training of deep neural networks to increase efficiency, i.e., to reduce the cost of