Search Results for author: Mohammad Azam Khan

Found 6 papers, 1 papers with code

WaveBound: Dynamic Error Bounds for Stable Time Series Forecasting

no code implementations25 Oct 2022 Youngin Cho, Daejin Kim, Dongmin Kim, Mohammad Azam Khan, Jaegul Choo

Time series forecasting has become a critical task due to its high practicality in real-world applications such as traffic, energy consumption, economics and finance, and disease analysis.

Time Series Forecasting

Guiding Users to Where to Give Color Hints for Efficient Interactive Sketch Colorization via Unsupervised Region Prioritization

no code implementations25 Oct 2022 Youngin Cho, Junsoo Lee, Soyoung Yang, Juntae Kim, Yeojeong Park, Haneol Lee, Mohammad Azam Khan, Daesik Kim, Jaegul Choo

Existing deep interactive colorization models have focused on ways to utilize various types of interactions, such as point-wise color hints, scribbles, or natural-language texts, as methods to reflect a user's intent at runtime.

Colorization Image Colorization

Mining Multi-Label Samples from Single Positive Labels

no code implementations12 Jun 2022 Youngin Cho, Daejin Kim, Mohammad Azam Khan, Jaegul Choo

Therefore, in this study we explore the practical setting called the single positive setting, where each data instance is annotated by only one positive label with no explicit negative labels.

Not Just Compete, but Collaborate: Local Image-to-Image Translation via Cooperative Mask Prediction

no code implementations CVPR 2021 Daejin Kim, Mohammad Azam Khan, Jaegul Choo

While the existing cycle-consistency loss ensures that the image can be translated back, our approach makes the model further preserve the attribute-irrelevant regions even in a single translation to another domain by using the Grad-CAM output computed from the discriminator.

Image-to-Image Translation Translation

Unsupervised Neural Machine Translation for Low-Resource Domains via Meta-Learning

no code implementations ACL 2021 Cheonbok Park, Yunwon Tae, Taehee Kim, Soyoung Yang, Mohammad Azam Khan, Eunjeong Park, Jaegul Choo

To address this issue, this paper presents a novel meta-learning algorithm for unsupervised neural machine translation (UNMT) that trains the model to adapt to another domain by utilizing only a small amount of training data.

General Knowledge Meta-Learning +3

Towards Lightweight Lane Detection by Optimizing Spatial Embedding

1 code implementation arXiv.org 2020 Seokwoo Jung, Sungha Choi, Mohammad Azam Khan, Jaegul Choo

This paper addresses the problem that pixel embedding in proposal-free instance segmentation based lane detection is difficult to optimize.

Instance Segmentation Lane Detection +2

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