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Yeachan Kim

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2 papers

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AAAI Conference 2023 Conference Paper

Dynamic Structure Pruning for Compressing CNNs

Jun-Hyung Park
Yeachan Kim
Junho Kim
Joon-Young Choi
SangKeun Lee

Structure pruning is an effective method to compress and accelerate neural networks. While filter and channel pruning are preferable to other structure pruning methods in terms of realistic acceleration and hardware compatibility, pruning methods with a finer granularity, such as intra-channel pruning, are expected to be capable of yielding more compact and computationally efficient networks. Typical intra-channel pruning methods utilize a static and hand-crafted pruning granularity due to a large search space, which leaves room for improvement in their pruning performance. In this work, we introduce a novel structure pruning method, termed as dynamic structure pruning, to identify optimal pruning granularities for intra-channel pruning. In contrast to existing intra-channel pruning methods, the proposed method automatically optimizes dynamic pruning granularities in each layer while training deep neural networks. To achieve this, we propose a differentiable group learning method designed to efficiently learn a pruning granularity based on gradient-based learning of filter groups. The experimental results show that dynamic structure pruning achieves state-of-the-art pruning performance and better realistic acceleration on a GPU compared with channel pruning. In particular, it reduces the FLOPs of ResNet50 by 71.85% without accuracy degradation on the ImageNet dataset. Our code is available at https://github.com/irishev/DSP.

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UAI Conference 2023 Conference Paper

Phase-shifted adversarial training

Yeachan Kim
Seongyeon Kim
Ihyeok Seo
Bonggun Shin

Adversarial training (AT) has been considered an imperative component for safely deploying neural network-based applications. However, it typically comes with slow convergence and worse performance on clean samples (i. e. , non-adversarial samples). In this work, we analyze the behavior of neural networks during learning with adversarial samples through the lens of response frequency. Interestingly, we observe that AT causes neural networks to converge slowly to high-frequency information, resulting in highly oscillatory predictions near each data point. To learn high-frequency content efficiently, we first prove that a universal phenomenon, the frequency principle (i. e. , lower frequencies are learned first), still holds in AT. Building upon this theoretical foundation, we present a novel approach to AT, which we call phase-shifted adversarial training (PhaseAT). In PhaseAT, the high-frequency components, which are a contributing factor to slow convergence, are adaptively shifted into the low-frequency range where faster convergence occurs. For evaluation, we conduct extensive experiments on CIFAR-10 and ImageNet, using an adaptive attack that is carefully designed for reliable evaluation. Comprehensive results show that PhaseAT substantially improves convergence for high-frequency information, thereby leading to improved adversarial robustness.

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