32 citations · 73 across the 10 of their papers we have counts for
10 papers
Diffusion-Based Conditional Image Editing through Optimized Inference with Guidance
Hyunsoo Lee, Minsoo Kang, Bohyung Han
We present a simple but effective training-free approach for text-driven image-to-image translation based on a pretrained text-to-image diffusion model. Our goal is to generate an…
Diffusion-Based Image-to-Image Translation by Noise Correction via Prompt Interpolation
Junsung Lee, Minsoo Kang, Bohyung Han
We propose a simple but effective training-free approach tailored to diffusion-based image-to-image translation. Our approach revises the original noise prediction network of a pre…
GuidedMixup: An Efficient Mixup Strategy Guided by Saliency Maps
Minsoo Kang, Suhyun Kim
Data augmentation is now an essential part of the image training process, as it effectively prevents overfitting and makes the model more robust against noisy datasets. Recent mixi…
Conditional Score Guidance for Text-Driven Image-to-Image Translation
Hyunsoo Lee, Minsoo Kang, Bohyung Han
We present a novel algorithm for text-driven image-to-image translation based on a pretrained text-to-image diffusion model. Our method aims to generate a target image by selective…
Variational Distribution Learning for Unsupervised Text-to-Image Generation
Minsoo Kang, Doyup Lee, Jiseob Kim +2
We propose a text-to-image generation algorithm based on deep neural networks when text captions for images are unavailable during training. In this work, instead of simply generat…
Information-Theoretic GAN Compression with Variational Energy-based Model
Minsoo Kang, Hyewon Yoo, Eunhee Kang +3
We propose an information-theoretic knowledge distillation approach for the compression of generative adversarial networks, which aims to maximize the mutual information between te…
Class-Incremental Learning by Knowledge Distillation with Adaptive Feature Consolidation
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 ta…
Class-Incremental Learning for Action Recognition in Videos
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 continu…
Operation-Aware Soft Channel Pruning using Differentiable Masks
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 operatio…
Towards Oracle Knowledge Distillation with Neural Architecture Search
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. Our approach addresses the…