614 citations · 2.5k across the 63 of their papers we have counts for
9 papers · 2 filters
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…
On the Stability-Plasticity Dilemma of Class-Incremental Learning
Dongwan Kim, Bohyung Han
A primary goal of class-incremental learning is to strike a balance between stability and plasticity, where models should be both stable enough to retain knowledge learned from pre…
Multi-Modal Representation Learning with Text-Driven Soft Masks
Jaeyoo Park, Bohyung Han
We propose a visual-linguistic representation learning approach within a self-supervised learning framework by introducing a new operation, loss, and data augmentation strategy. Fi…
ContraNeRF: 3D-Aware Generative Model via Contrastive Learning with Unsupervised Implicit Pose Embedding
Mijeong Kim, Hyunjoon Lee, Bohyung Han
Although 3D-aware GANs based on neural radiance fields have achieved competitive performance, their applicability is still limited to objects or scenes with the ground-truths or pr…
Randomized Adversarial Style Perturbations for Domain Generalization
Taehoon Kim, Bohyung Han
We propose a novel domain generalization technique, referred to as Randomized Adversarial Style Perturbation (RASP), which is motivated by the observation that the characteristics…
Cross-Class Feature Augmentation for Class Incremental Learning
Taehoon Kim, Jaeyoo Park, Bohyung Han
We propose a novel class incremental learning approach by incorporating a feature augmentation technique motivated by adversarial attacks. We employ a classifier learned in the pas…
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…
Metric Compatible Training for Online Backfilling in Large-Scale Retrieval
Seonguk Seo, Mustafa Gokhan Uzunbas, Bohyung Han +2
Backfilling is the process of re-extracting all gallery embeddings from upgraded models in image retrieval systems. It inevitably requires a prohibitively large amount of computati…