380 citations · 1.6k across the 84 of their papers we have counts for
11 papers · 1 filter
Pre-Training of Deep Bidirectional Protein Sequence Representations with Structural Information
Seonwoo Min, Seunghyun Park, Siwon Kim +3
Bridging the exponentially growing gap between the numbers of unlabeled and labeled protein sequences, several studies adopted semi-supervised learning for protein sequence modelin…
Frame-to-Frame Aggregation of Active Regions in Web Videos for Weakly Supervised Semantic Segmentation
Jungbeom Lee, Eunji Kim, Sungmin Lee +2
When a deep neural network is trained on data with only image-level labeling, the regions activated in each image tend to identify only a small region of the target object. We prop…
One-Shot Learning for Text-to-SQL Generation
Dongjun Lee, Jaesik Yoon, Jongyun Song +2
Most deep learning approaches for text-to-SQL generation are limited to the WikiSQL dataset, which only supports very simple queries. Recently, template-based and sequence-to-seque…
Learning Condensed and Aligned Features for Unsupervised Domain Adaptation Using Label Propagation
Jaeyoon Yoo, Changhwa Park, Yongjun Hong +1
Unsupervised domain adaptation aiming to learn a specific task for one domain using another domain data has emerged to address the labeling issue in supervised learning, especially…
PuVAE: A Variational Autoencoder to Purify Adversarial Examples
Uiwon Hwang, Jaewoo Park, Hyemi Jang +2
Deep neural networks are widely used and exhibit excellent performance in many areas. However, they are vulnerable to adversarial attacks that compromise the network at the inferen…
PixelSteganalysis: Destroying Hidden Information with a Low Degree of Visual Degradation
Dahuin Jung, Ho Bae, Hyun-Soo Choi +1
Steganography is the science of unnoticeably concealing a secret message within a certain image, called a cover image. The cover image with the secret message is called a stego ima…
FickleNet: Weakly and Semi-supervised Semantic Image Segmentation using Stochastic Inference
Jungbeom Lee, Eunji Kim, Sungmin Lee +2
The main obstacle to weakly supervised semantic image segmentation is the difficulty of obtaining pixel-level information from coarse image-level annotations. Most methods based on…
Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detection
Seijoon Kim, Seongsik Park, Byunggook Na +1
Over the past decade, deep neural networks (DNNs) have demonstrated remarkable performance in a variety of applications. As we try to solve more advanced problems, increasing deman…
PixelSteganalysis: Pixel-wise Hidden Information Removal with Low Visual Degradation
Dahuin Jung, Ho Bae, Hyun-Soo Choi +1
Recently, the field of steganography has experienced rapid developments based on deep learning (DL). DL based steganography distributes secret information over all the available bi…
HexaGAN: Generative Adversarial Nets for Real World Classification
Uiwon Hwang, Dahuin Jung, Sungroh Yoon
Most deep learning classification studies assume clean data. However, when dealing with the real world data, we encounter three problems such as 1) missing data, 2) class imbalance…
AnomiGAN: Generative adversarial networks for anonymizing private medical data
Ho Bae, Dahuin Jung, Sungroh Yoon
Typical personal medical data contains sensitive information about individuals. Storing or sharing the personal medical data is thus often risky. For example, a short DNA sequence…