86 citations · 203 across the 12 of their papers we have counts for
12 papers
The Role of Teacher Calibration in Knowledge Distillation
Suyoung Kim, Seonguk Park, Junhoo Lee +1
Knowledge Distillation (KD) has emerged as an effective model compression technique in deep learning, enabling the transfer of knowledge from a large teacher model to a compact stu…
Semantics-Guided Object Removal for Facial Images: with Broad Applicability and Robust Style Preservation
Jookyung Song, Yeonjin Chang, Seonguk Park +1
Object removal and image inpainting in facial images is a task in which objects that occlude a facial image are specifically targeted, removed, and replaced by a properly reconstru…
Local-Selective Feature Distillation for Single Image Super-Resolution
SeongUk Park, Nojun Kwak
Recent improvements in convolutional neural network (CNN)-based single image super-resolution (SISR) methods rely heavily on fabricating network architectures, rather than finding…
On the Orthogonality of Knowledge Distillation with Other Techniques: From an Ensemble Perspective
SeongUk Park, KiYoon Yoo, Nojun Kwak
To put a state-of-the-art neural network to practical use, it is necessary to design a model that has a good trade-off between the resource consumption and performance on the test…
Diverse and Admissible Trajectory Forecasting through Multimodal Context Understanding
Seong Hyeon Park, Gyubok Lee, Manoj Bhat +6
Multi-agent trajectory forecasting in autonomous driving requires an agent to accurately anticipate the behaviors of the surrounding vehicles and pedestrians, for safe and reliable…
Feature-map-level Online Adversarial Knowledge Distillation
Inseop Chung, SeongUk Park, Jangho Kim +1
Feature maps contain rich information about image intensity and spatial correlation. However, previous online knowledge distillation methods only utilize the class probabilities. T…
FEED: Feature-level Ensemble for Knowledge Distillation
SeongUk Park, Nojun Kwak
Knowledge Distillation (KD) aims to transfer knowledge in a teacher-student framework, by providing the predictions of the teacher network to the student network in the training st…
ScarfNet: Multi-scale Features with Deeply Fused and Redistributed Semantics for Enhanced Object Detection
Jin Hyeok Yoo, Dongsuk Kum, Jun Won Choi
Convolutional neural network (CNN) has led to significant progress in object detection. In order to detect the objects in various sizes, the object detectors often exploit the hier…
Sym-parameterized Dynamic Inference for Mixed-Domain Image Translation
Simyung Chang, SeongUk Park, John Yang +1
Recent advances in image-to-image translation have led to some ways to generate multiple domain images through a single network. However, there is still a limit in creating an imag…
Paraphrasing Complex Network: Network Compression via Factor Transfer
Jangho Kim, SeongUk Park, Nojun Kwak
Many researchers have sought ways of model compression to reduce the size of a deep neural network (DNN) with minimal performance degradation in order to use DNNs in embedded syste…
Butterfly Effect: Bidirectional Control of Classification Performance by Small Additive Perturbation
YoungJoon Yoo, Seonguk Park, Junyoung Choi +2
This paper proposes a new algorithm for controlling classification results by generating a small additive perturbation without changing the classifier network. Our work is inspired…
Broadcasting Convolutional Network for Visual Relational Reasoning
Simyung Chang, John Yang, Seonguk Park +1
In this paper, we propose the Broadcasting Convolutional Network (BCN) that extracts key object features from the global field of an entire input image and recognizes their relatio…