1 code implementation • NeurIPS 2021 • Jaeho Lee, Jihoon Tack, Namhoon Lee, Jinwoo Shin
Implicit neural representations are a promising new avenue of representing general signals by learning a continuous function that, parameterized as a neural network, maps the domain of a signal to its codomain; the mapping from spatial coordinates of an image to its pixel values, for example.
no code implementations • ICLR 2021 • Namhoon Lee, Thalaiyasingam Ajanthan, Philip H. S. Torr, Martin Jaggi
As a result, we find across various workloads of data set, network model, and optimization algorithm that there exists a general scaling trend between batch size and number of training steps to convergence for the effect of data parallelism, and further, difficulty of training under sparsity.
1 code implementation • ICLR 2020 • Namhoon Lee, Thalaiyasingam Ajanthan, Stephen Gould, Philip H. S. Torr
Alternatively, a recent approach shows that pruning can be done at initialization prior to training, based on a saliency criterion called connection sensitivity.
6 code implementations • ICLR 2019 • Namhoon Lee, Thalaiyasingam Ajanthan, Philip H. S. Torr
To achieve this, we introduce a saliency criterion based on connection sensitivity that identifies structurally important connections in the network for the given task.
4 code implementations • ICLR 2018 • Saumya Jetley, Nicholas A. Lord, Namhoon Lee, Philip H. S. Torr
We propose an end-to-end-trainable attention module for convolutional neural network (CNN) architectures built for image classification.
3 code implementations • CVPR 2017 • Namhoon Lee, Wongun Choi, Paul Vernaza, Christopher B. Choy, Philip H. S. Torr, Manmohan Chandraker
DESIRE effectively predicts future locations of objects in multiple scenes by 1) accounting for the multi-modal nature of the future prediction (i. e., given the same context, future may vary), 2) foreseeing the potential future outcomes and make a strategic prediction based on that, and 3) reasoning not only from the past motion history, but also from the scene context as well as the interactions among the agents.
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no code implementations • 15 Dec 2016 • Namhoon Lee, Xinshuo Weng, Vishnu Naresh Boddeti, Yu Zhang, Fares Beainy, Kris Kitani, Takeo Kanade
We introduce the concept of a Visual Compiler that generates a scene specific pedestrian detector and pose estimator without any pedestrian observations.
no code implementations • CVPR 2017 • Wei-Chiu Ma, De-An Huang, Namhoon Lee, Kris M. Kitani
We develop predictive models of pedestrian dynamics by encoding the coupled nature of multi-pedestrian interaction using game theory, and deep learning-based visual analysis to estimate person-specific behavior parameters.