614 citations · 2.5k across the 63 of their papers we have counts for
11 papers · 1 filter
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-…
Towards Oracle Knowledge Distillation with Neural Architecture Search
Minsoo Kang, Jonghwan Mun, Bohyung Han
We present a novel framework of knowledge distillation that is capable of learning powerful and efficient student models from ensemble teacher networks. Our approach addresses the…
Reinforcing an Image Caption Generator Using Off-Line Human Feedback
Paul Hongsuck Seo, Piyush Sharma, Tomer Levinboim +2
Human ratings are currently the most accurate way to assess the quality of an image captioning model, yet most often the only used outcome of an expensive human rating evaluation i…
Fine-Grained Neural Architecture Search
Heewon Kim, Seokil Hong, Bohyung Han +2
We present an elegant framework of fine-grained neural architecture search (FGNAS), which allows to employ multiple heterogeneous operations within a single layer and can even gene…
Efficient Decoupled Neural Architecture Search by Structure and Operation Sampling
Heung-Chang Lee, Do-Guk Kim, Bohyung Han
We propose a novel neural architecture search algorithm via reinforcement learning by decoupling structure and operation search processes. Our approach samples candidate models fro…
Regularizing Neural Networks via Stochastic Branch Layers
Wonpyo Park, Paul Hongsuck Seo, Bohyung Han +1
We introduce a novel stochastic regularization technique for deep neural networks, which decomposes a layer into multiple branches with different parameters and merges stochastical…
Continual Learning by Asymmetric Loss Approximation with Single-Side Overestimation
Dongmin Park, Seokil Hong, Bohyung Han +1
Catastrophic forgetting is a critical challenge in training deep neural networks. Although continual learning has been investigated as a countermeasure to the problem, it often suf…
Learning to Optimize Domain Specific Normalization for Domain Generalization
Seonguk Seo, Yumin Suh, Dongwan Kim +3
We propose a simple but effective multi-source domain generalization technique based on deep neural networks by incorporating optimized normalization layers that are specific to in…
Domain-Specific Batch Normalization for Unsupervised Domain Adaptation
Woong-Gi Chang, Tackgeun You, Seonguk Seo +2
We propose a novel unsupervised domain adaptation framework based on domain-specific batch normalization in deep neural networks. We aim to adapt to both domains by specializing ba…
Context-Aware Zero-Shot Recognition
Ruotian Luo, Ning Zhang, Bohyung Han +1
We present a novel problem setting in zero-shot learning, zero-shot object recognition and detection in the context. Contrary to the traditional zero-shot learning methods, which s…
Streamlined Dense Video Captioning
Jonghwan Mun, Linjie Yang, Zhou Ren +2
Dense video captioning is an extremely challenging task since accurate and coherent description of events in a video requires holistic understanding of video contents as well as co…