5 citations · 10 across the 5 of their papers we have counts for
5 papers
On the Robustness of Normalizing Flows for Inverse Problems in Imaging
Seongmin Hong, Inbum Park, Se Young Chun
Conditional normalizing flows can generate diverse image samples for solving inverse problems. Most normalizing flows for inverse problems in imaging employ the conditional affine…
Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: Report
Andrey Ignatov, Radu Timofte, Maurizio Denna +93
Image super-resolution is a common task on mobile and IoT devices, where one often needs to upscale and enhance low-resolution images and video frames. While numerous solutions hav…
Efficient Single-Image Depth Estimation on Mobile Devices, Mobile AI & AIM 2022 Challenge: Report
Andrey Ignatov, Grigory Malivenko, Radu Timofte +36
Various depth estimation models are now widely used on many mobile and IoT devices for image segmentation, bokeh effect rendering, object tracking and many other mobile tasks. Thus…
DFX: A Low-latency Multi-FPGA Appliance for Accelerating Transformer-based Text Generation
Seongmin Hong, Seungjae Moon, Junsoo Kim +4
Transformer is a deep learning language model widely used for natural language processing (NLP) services in datacenters. Among transformer models, Generative Pre-trained Transforme…
FIXAR: A Fixed-Point Deep Reinforcement Learning Platform with Quantization-Aware Training and Adaptive Parallelism
Je Yang, Seongmin Hong, Joo-Young Kim
In this paper, we present a deep reinforcement learning platform named FIXAR which employs fixed-point data types and arithmetic units for the first time using a SW/HW co-design ap…