380 citations · 1.6k across the 84 of their papers we have counts for
23 papers · 1 filter
Robust End-to-End Focal Liver Lesion Detection using Unregistered Multiphase Computed Tomography Images
Sang-gil Lee, Eunji Kim, Jae Seok Bae +2
The computer-aided diagnosis of focal liver lesions (FLLs) can help improve workflow and enable correct diagnoses; FLL detection is the first step in such a computer-aided diagnosi…
Supervised Neural Discrete Universal Denoiser for Adaptive Denoising
Sungmin Cha, Seonwoo Min, Sungroh Yoon +1
We improve the recently developed Neural DUDE, a neural network-based adaptive discrete denoiser, by combining it with the supervised learning framework. Namely, we make the superv…
Guided-TTS: A Diffusion Model for Text-to-Speech via Classifier Guidance
Heeseung Kim, Sungwon Kim, Sungroh Yoon
We propose Guided-TTS, a high-quality text-to-speech (TTS) model that does not require any transcript of target speaker using classifier guidance. Guided-TTS combines an unconditio…
Scalable Smartphone Cluster for Deep Learning
Byunggook Na, Jaehee Jang, Seongsik Park +7
Various deep learning applications on smartphones have been rapidly rising, but training deep neural networks (DNNs) has too large computational burden to be executed on a single s…
Reducing Information Bottleneck for Weakly Supervised Semantic Segmentation
Jungbeom Lee, Jooyoung Choi, Jisoo Mok +1
Weakly supervised semantic segmentation produces pixel-level localization from class labels; however, a classifier trained on such labels is likely to focus on a small discriminati…
FICGAN: Facial Identity Controllable GAN for De-identification
Yonghyun Jeong, Jooyoung Choi, Sungwon Kim +5
In this work, we present Facial Identity Controllable GAN (FICGAN) for not only generating high-quality de-identified face images with ensured privacy protection, but also detailed…
ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models
Jooyoung Choi, Sungwon Kim, Yonghyun Jeong +2
Denoising diffusion probabilistic models (DDPM) have shown remarkable performance in unconditional image generation. However, due to the stochasticity of the generative process in…
AligNART: Non-autoregressive Neural Machine Translation by Jointly Learning to Estimate Alignment and Translate
Jongyoon Song, Sungwon Kim, Sungroh Yoon
Non-autoregressive neural machine translation (NART) models suffer from the multi-modality problem which causes translation inconsistency such as token repetition. Most recent appr…
Towards a Rigorous Evaluation of Time-series Anomaly Detection
Siwon Kim, Kukjin Choi, Hyun-Soo Choi +2
In recent years, proposed studies on time-series anomaly detection (TAD) report high F1 scores on benchmark TAD datasets, giving the impression of clear improvements in TAD. Howeve…
AdvRush: Searching for Adversarially Robust Neural Architectures
Jisoo Mok, Byunggook Na, Hyeokjun Choe +1
Deep neural networks continue to awe the world with their remarkable performance. Their predictions, however, are prone to be corrupted by adversarial examples that are imperceptib…
Toward Spatially Unbiased Generative Models
Jooyoung Choi, Jungbeom Lee, Yonghyun Jeong +1
Recent image generation models show remarkable generation performance. However, they mirror strong location preference in datasets, which we call spatial bias. Therefore, generator…
TargetNet: Functional microRNA Target Prediction with Deep Neural Networks
Seonwoo Min, Byunghan Lee, Sungroh Yoon
Motivation: MicroRNAs (miRNAs) play pivotal roles in gene expression regulation by binding to target sites of messenger RNAs (mRNAs). While identifying functional targets of miRNAs…
Flexible dual-branched message passing neural network for quantum mechanical property prediction with molecular conformation
Jeonghee Jo, Bumju Kwak, Byunghan Lee +1
A molecule is a complex of heterogeneous components, and the spatial arrangements of these components determine the whole molecular properties and characteristics. With the advent…
Accelerating Neural Architecture Search via Proxy Data
Byunggook Na, Jisoo Mok, Hyeokjun Choe +1
Despite the increasing interest in neural architecture search (NAS), the significant computational cost of NAS is a hindrance to researchers. Hence, we propose to reduce the cost o…
Training Energy-Efficient Deep Spiking Neural Networks with Time-to-First-Spike Coding
Seongsik Park, Sungroh Yoon
The tremendous energy consumption of deep neural networks (DNNs) has become a serious problem in deep learning. Spiking neural networks (SNNs), which mimic the operations in the hu…
GeoT: A Geometry-aware Transformer for Reliable Molecular Property Prediction and Chemically Interpretable Representation Learning
Bumju Kwak, Jiwon Park, Taewon Kang +3
In recent years, molecular representation learning has emerged as a key area of focus in various chemical tasks. However, many existing models fail to fully consider the geometric…
Energy-efficient Knowledge Distillation for Spiking Neural Networks
Dongjin Lee, Seongsik Park, Jongwan Kim +2
Spiking neural networks (SNNs) have been gaining interest as energy-efficient alternatives of conventional artificial neural networks (ANNs) due to their event-driven computation.…
Stein Latent Optimization for Generative Adversarial Networks
Uiwon Hwang, Heeseung Kim, Dahuin Jung +3
Generative adversarial networks (GANs) with clustered latent spaces can perform conditional generation in a completely unsupervised manner. In the real world, the salient attribute…
Noise-Robust Deep Spiking Neural Networks with Temporal Information
Seongsik Park, Dongjin Lee, Sungroh Yoon
Spiking neural networks (SNNs) have emerged as energy-efficient neural networks with temporal information. SNNs have shown a superior efficiency on neuromorphic devices, but the de…
XProtoNet: Diagnosis in Chest Radiography with Global and Local Explanations
Eunji Kim, Siwon Kim, Minji Seo +1
Automated diagnosis using deep neural networks in chest radiography can help radiologists detect life-threatening diseases. However, existing methods only provide predictions witho…
BBAM: Bounding Box Attribution Map for Weakly Supervised Semantic and Instance Segmentation
Jungbeom Lee, Jihun Yi, Chaehun Shin +1
Weakly supervised segmentation methods using bounding box annotations focus on obtaining a pixel-level mask from each box containing an object. Existing methods typically depend on…
Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentation
Jungbeom Lee, Eunji Kim, Sungroh Yoon
Weakly supervised semantic segmentation produces a pixel-level localization from a classifier, but it is likely to restrict its focus to a small discriminative region of the target…
Removing Undesirable Feature Contributions Using Out-of-Distribution Data
Saehyung Lee, Changhwa Park, Hyungyu Lee +3
Several data augmentation methods deploy unlabeled-in-distribution (UID) data to bridge the gap between the training and inference of neural networks. However, these methods have c…