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Jinyoung Choi

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8 papers

2 author rows

ICLR Conference 2025 Conference Paper

Enhanced Diffusion Sampling via Extrapolation with Multiple ODE Solutions

Jinyoung Choi
Junoh Kang
Bohyung Han

Diffusion probabilistic models (DPMs), while effective in generating high-quality samples, often suffer from high computational costs due to their iterative sampling process. To address this, we propose an enhanced ODE-based sampling method for DPMs inspired by Richardson extrapolation, which reduces numerical error and improves convergence rates. Our method, RX-DPM, leverages multiple ODE solutions at intermediate time steps to extrapolate the denoised prediction in DPMs. This significantly enhances the accuracy of estimations for the final sample while maintaining the number of function evaluations (NFEs). Unlike standard Richardson extrapolation, which assumes uniform discretization of the time grid, we develop a more general formulation tailored to arbitrary time step scheduling, guided by local truncation error derived from a baseline sampling method. The simplicity of our approach facilitates accurate estimation of numerical solutions without significant computational overhead, and allows for seamless and convenient integration into various DPMs and solvers. Additionally, RX-DPM provides explicit error estimates, effectively demonstrating the faster convergence as the leading error term's order increases. Through a series of experiments, we show that the proposed method improves the quality of generated samples without requiring additional sampling iterations.

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NeurIPS Conference 2024 Conference Paper

FIFO-Diffusion: Generating Infinite Videos from Text without Training

Jihwan Kim
Junoh Kang
Jinyoung Choi
Bohyung Han

We propose a novel inference technique based on a pretrained diffusion model for text-conditional video generation. Our approach, called FIFO-Diffusion, is conceptually capable of generating infinitely long videos without additional training. This is achieved by iteratively performing diagonal denoising, which simultaneously processes a series of consecutive frames with increasing noise levels in a queue; our method dequeues a fully denoised frame at the head while enqueuing a new random noise frame at the tail. However, diagonal denoising is a double-edged sword as the frames near the tail can take advantage of cleaner frames by forward reference but such a strategy induces the discrepancy between training and inference. Hence, we introduce latent partitioning to reduce the training-inference gap and lookahead denoising to leverage the benefit of forward referencing. Practically, FIFO-Diffusion consumes a constant amount of memory regardless of the target video length given a baseline model, while well-suited for parallel inference on multiple GPUs. We have demonstrated the promising results and effectiveness of the proposed methods on existing text-to-video generation baselines. Generated video examples and source codes are available at our project page.

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NeurIPS Conference 2022 Conference Paper

MCL-GAN: Generative Adversarial Networks with Multiple Specialized Discriminators

Jinyoung Choi
Bohyung Han

We propose a framework of generative adversarial networks with multiple discriminators, which collaborate to represent a real dataset more effectively. Our approach facilitates learning a generator consistent with the underlying data distribution based on real images and thus mitigates the chronic mode collapse problem. From the inspiration of multiple choice learning, we guide each discriminator to have expertise in a subset of the entire data and allow the generator to find reasonable correspondences between the latent and real data spaces automatically without extra supervision for training examples. Despite the use of multiple discriminators, the backbone networks are shared across the discriminators and the increase in training cost is marginal. We demonstrate the effectiveness of our algorithm using multiple evaluation metrics in the standard datasets for diverse tasks.

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ICRA Conference 2021 Conference Paper

Risk-Conditioned Distributional Soft Actor-Critic for Risk-Sensitive Navigation

Jinyoung Choi
Christopher R. Dance
Jung-Eun Kim
Seulbin Hwang
Kyungsik Park

Modern navigation algorithms based on deep reinforcement learning (RL) show promising efficiency and robustness. However, most deep RL algorithms operate in a risk-neutral manner, making no special attempt to shield users from relatively rare but serious outcomes, even if such shielding might cause little loss of performance. Furthermore, such algorithms typically make no provisions to ensure safety in the presence of inaccuracies in the models on which they were trained, beyond adding a cost-of-collision and some domain randomization while training, in spite of the formidable complexity of the environments in which they operate. In this paper, we present a novel distributional RL algorithm that not only learns an uncertainty-aware policy, but can also change its risk measure without expensive fine-tuning or retraining. Our method shows superior performance and safety over baselines in partially- observed navigation tasks. We also demonstrate that agents trained using our method can adapt their policies to a wide range of risk measures at run-time.

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ICRA Conference 2020 Conference Paper

Fast Adaptation of Deep Reinforcement Learning-Based Navigation Skills to Human Preference

Jinyoung Choi
Christopher R. Dance
Jung-Eun Kim
Kyungsik Park
Jaehun Han
Joonho Seo
Minsu Kim

Deep reinforcement learning (RL) is being actively studied for robot navigation due to its promise of superior performance and robustness. However, most existing deep RL navigation agents are trained using fixed parameters, such as maximum velocities and weightings of reward components. Since the optimal choice of parameters depends on the use-case, it can be difficult to deploy such existing methods in a variety of real-world service scenarios. In this paper, we propose a novel deep RL navigation method that can adapt its policy to a wide range of parameters and reward functions without expensive retraining. Additionally, we explore a Bayesian deep learning method to optimize these parameters that requires only a small amount of preference data. We empirically show that our method can learn diverse navigation skills and quickly adapt its policy to a given performance metric or to human preference. We also demonstrate our method in real-world scenarios.

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ICRA Conference 2019 Conference Paper

Deep Reinforcement Learning of Navigation in a Complex and Crowded Environment with a Limited Field of View

Jinyoung Choi
Kyungsik Park
Minsu Kim
Sangok Seok

Mobile robots are required to navigate freely in a complex and crowded environment in order to provide services to humans. For this navigation ability, deep reinforcement learning (DRL)-based methods are gaining increasing attentions. However, existing DRL methods require a wide field of view (FOV), which imposes the usage of high-cost lidar devices. In this paper, we explore the possibility of replacing expensive lidar devices with affordable depth cameras which have a limited FOV. First, we analyze the effect of a limited field of view in the DRL agents. Second, we propose a LSTM agent with Local-Map Critic (LSTM-LMC), which is a novel DRL method to learn efficient navigation in a complex environment with a limited FOV. Lastly, we introduce the dynamics randomization technique to improve the robustness of the DRL agents in the real world. We found that our method with a limited FOV can outperform the methods having a wide FOV but limited memory. We provide the empirical evidence that our method learns to implicitly model the surrounding environment and dynamics of other agents. We also show that a robot with a single depth camera can navigate through a complex real-world environment using our method.

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AAAI Conference 2018 System Paper

Perception-Action-Learning System for Mobile Social-Service Robots Using Deep Learning

Beom-Jin Lee
Jinyoung Choi
Chung-Yeon Lee
Kyung-Wha Park
Sungjun Choi
Cheolho Han
Dong-Sig Han
Christina Baek

We introduce a robust integrated perception-action-learning system for mobile social-service robots. The state-of-the-art deep learning techniques were incorporated into each module which signiﬁcantly improves the performance in solving social service tasks. The system not only demonstrated fast and robust performance in a homelike environment but also achieved the highest score in the RoboCup2017@Home Social Standard Platform League (SSPL) held in Nagoya, Japan.

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ICRA Conference 2018 Conference Paper

Robust Human Following by Deep Bayesian Trajectory Prediction for Home Service Robots

Beom-Jin Lee
Jinyoung Choi
Christina Baek
Byoung-Tak Zhang

The capability of following a person is crucial in service-oriented robots for human assistance and cooperation. Though a vast variety of following systems exist, they lack robustness against dynamic changes of the environment and relocating to continue following a lost target. Here we present a robust human following system that has the extendability to commercial service robot platforms having a RGB-D camera. The proposed framework integrates deep learning methods for perception and variational Bayesian techniques for trajectory prediction. Deep learning modules enable robots to accompany a person by detecting the target, learning the target and following while avoiding collision within the dynamic home environment. The variational Bayesian techniques robustly predict the trajectory of the target by empowering the following ability of the robot when target is lost. We experimentally demonstrate the capability of the deep Bayesian trajectory prediction method on real-time usage, following abilities, collision avoidance and trajectory prediction of the system. The proposed system was deployed at the RoboCup@Home 2017 Social Standard Platform League and successfully demonstrated its robust functions and smooth person following capability resulting in winning the 1st place.

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