614 citations · 1.1k across the 11 of their papers we have counts for
11 papers
Asymmetric self-play for automatic goal discovery in robotic manipulation
OpenAI OpenAI, Matthias Plappert, Raul Sampedro +13
We train a single, goal-conditioned policy that can solve many robotic manipulation tasks, including tasks with previously unseen goals and objects. We rely on asymmetric self-play…
Behavior Priors for Efficient Reinforcement Learning
Dhruva Tirumala, Alexandre Galashov, Hyeonwoo Noh +8
As we deploy reinforcement learning agents to solve increasingly challenging problems, methods that allow us to inject prior knowledge about the structure of the world and effectiv…
Real-Time Object Tracking via Meta-Learning: Efficient Model Adaptation and One-Shot Channel Pruning
Ilchae Jung, Kihyun You, Hyeonwoo Noh +2
We propose a novel meta-learning framework for real-time object tracking with efficient model adaptation and channel pruning. Given an object tracker, our framework learns to fine-…
Exploiting Hierarchy for Learning and Transfer in KL-regularized RL
Dhruva Tirumala, Hyeonwoo Noh, Alexandre Galashov +6
As reinforcement learning agents are tasked with solving more challenging and diverse tasks, the ability to incorporate prior knowledge into the learning system and to exploit reus…
Transfer Learning via Unsupervised Task Discovery for Visual Question Answering
Hyeonwoo Noh, Taehoon Kim, Jonghwan Mun +1
We study how to leverage off-the-shelf visual and linguistic data to cope with out-of-vocabulary answers in visual question answering task. Existing large-scale visual datasets wit…
Regularizing Deep Neural Networks by Noise: Its Interpretation and Optimization
Hyeonwoo Noh, Tackgeun You, Jonghwan Mun +1
Overfitting is one of the most critical challenges in deep neural networks, and there are various types of regularization methods to improve generalization performance. Injecting n…
Large-Scale Image Retrieval with Attentive Deep Local Features
Hyeonwoo Noh, Andre Araujo, Jack Sim +2
We propose an attentive local feature descriptor suitable for large-scale image retrieval, referred to as DELF (DEep Local Feature). The new feature is based on convolutional neura…
Training Recurrent Answering Units with Joint Loss Minimization for VQA
Hyeonwoo Noh, Bohyung Han
We propose a novel algorithm for visual question answering based on a recurrent deep neural network, where every module in the network corresponds to a complete answering unit with…
Image Question Answering using Convolutional Neural Network with Dynamic Parameter Prediction
Hyeonwoo Noh, Paul Hongsuck Seo, Bohyung Han
We tackle image question answering (ImageQA) problem by learning a convolutional neural network (CNN) with a dynamic parameter layer whose weights are determined adaptively based o…
Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation
Seunghoon Hong, Hyeonwoo Noh, Bohyung Han
We propose a novel deep neural network architecture for semi-supervised semantic segmentation using heterogeneous annotations. Contrary to existing approaches posing semantic segme…
Learning Deconvolution Network for Semantic Segmentation
Hyeonwoo Noh, Seunghoon Hong, Bohyung Han
We propose a novel semantic segmentation algorithm by learning a deconvolution network. We learn the network on top of the convolutional layers adopted from VGG 16-layer net. The d…