no code implementations • 24 Jan 2024 • Minsoo Kang, Minkoo Kang, Suhyun Kim
Deep learning has made significant advances in computer vision, particularly in image classification tasks.
1 code implementation • 29 Jun 2023 • Minsoo Kang, Suhyun Kim
From this motivation, we propose a novel saliency-aware mixup method, GuidedMixup, which aims to retain the salient regions in mixup images with low computational overhead.
no code implementations • CVPR 2023 • Minsoo Kang, Doyup Lee, Jiseob Kim, Saehoon Kim, Bohyung Han
We propose a text-to-image generation algorithm based on deep neural networks when text captions for images are unavailable during training.
no code implementations • 28 Mar 2023 • Minsoo Kang, Hyewon Yoo, Eunhee Kang, Sehwan Ki, Hyong-Euk Lee, Bohyung Han
We propose an information-theoretic knowledge distillation approach for the compression of generative adversarial networks, which aims to maximize the mutual information between teacher and student networks via a variational optimization based on an energy-based model.
1 code implementation • CVPR 2022 • Minsoo Kang, Jaeyoo Park, Bohyung Han
We present a novel class incremental learning approach based on deep neural networks, which continually learns new tasks with limited memory for storing examples in the previous tasks.
no code implementations • ICCV 2021 • Jaeyoo Park, Minsoo Kang, Bohyung Han
We tackle catastrophic forgetting problem in the context of class-incremental learning for video recognition, which has not been explored actively despite the popularity of continual learning.
1 code implementation • ICML 2020 • Minsoo Kang, Bohyung Han
We propose a simple but effective data-driven channel pruning algorithm, which compresses deep neural networks in a differentiable way by exploiting the characteristics of operations.
no code implementations • 29 Nov 2019 • Minsoo Kang, Jonghwan Mun, Bohyung Han
We present a novel framework of knowledge distillation that is capable of learning powerful and efficient student models from ensemble teacher networks.