146 research outputs found
Deep Virtual Networks for Memory Efficient Inference of Multiple Tasks
Deep networks consume a large amount of memory by their nature. A natural
question arises can we reduce that memory requirement whilst maintaining
performance. In particular, in this work we address the problem of memory
efficient learning for multiple tasks. To this end, we propose a novel network
architecture producing multiple networks of different configurations, termed
deep virtual networks (DVNs), for different tasks. Each DVN is specialized for
a single task and structured hierarchically. The hierarchical structure, which
contains multiple levels of hierarchy corresponding to different numbers of
parameters, enables multiple inference for different memory budgets. The
building block of a deep virtual network is based on a disjoint collection of
parameters of a network, which we call a unit. The lowest level of hierarchy in
a deep virtual network is a unit, and higher levels of hierarchy contain lower
levels' units and other additional units. Given a budget on the number of
parameters, a different level of a deep virtual network can be chosen to
perform the task. A unit can be shared by different DVNs, allowing multiple
DVNs in a single network. In addition, shared units provide assistance to the
target task with additional knowledge learned from another tasks. This
cooperative configuration of DVNs makes it possible to handle different tasks
in a memory-aware manner. Our experiments show that the proposed method
outperforms existing approaches for multiple tasks. Notably, ours is more
efficient than others as it allows memory-aware inference for all tasks.Comment: CVPR 201
Deep Elastic Networks with Model Selection for Multi-Task Learning
In this work, we consider the problem of instance-wise dynamic network model
selection for multi-task learning. To this end, we propose an efficient
approach to exploit a compact but accurate model in a backbone architecture for
each instance of all tasks. The proposed method consists of an estimator and a
selector. The estimator is based on a backbone architecture and structured
hierarchically. It can produce multiple different network models of different
configurations in a hierarchical structure. The selector chooses a model
dynamically from a pool of candidate models given an input instance. The
selector is a relatively small-size network consisting of a few layers, which
estimates a probability distribution over the candidate models when an input
instance of a task is given. Both estimator and selector are jointly trained in
a unified learning framework in conjunction with a sampling-based learning
strategy, without additional computation steps. We demonstrate the proposed
approach for several image classification tasks compared to existing approaches
performing model selection or learning multiple tasks. Experimental results
show that our approach gives not only outstanding performance compared to other
competitors but also the versatility to perform instance-wise model selection
for multiple tasks.Comment: ICCV 201
Learning to Discriminate Information for Online Action Detection
From a streaming video, online action detection aims to identify actions in
the present. For this task, previous methods use recurrent networks to model
the temporal sequence of current action frames. However, these methods overlook
the fact that an input image sequence includes background and irrelevant
actions as well as the action of interest. For online action detection, in this
paper, we propose a novel recurrent unit to explicitly discriminate the
information relevant to an ongoing action from others. Our unit, named
Information Discrimination Unit (IDU), decides whether to accumulate input
information based on its relevance to the current action. This enables our
recurrent network with IDU to learn a more discriminative representation for
identifying ongoing actions. In experiments on two benchmark datasets, TVSeries
and THUMOS-14, the proposed method outperforms state-of-the-art methods by a
significant margin. Moreover, we demonstrate the effectiveness of our recurrent
unit by conducting comprehensive ablation studies.Comment: To appear in CVPR 202
Data Diversification Analysis on Data Preprocessing
A statistical analysis to examine the diversity distribution resulting from two different approaches: The first one, the standard approach, is a baseline augmentation approach where a random augmentation is applied to each sample in each epoch independently; The second one, the random batch approach, is another new augmentation approach designed where a random augmentation is applied to each tiny-batch in each epoch independently, and which samples are in the same tiny-batch is random and independent across all epochs
A broadband X-ray study of the Rabbit pulsar wind nebula powered by PSR J1418-6058
We report on broadband X-ray properties of the Rabbit pulsar wind nebula
(PWN) associated with the pulsar PSR J1418-6058 using archival Chandra and
XMM-Newton data, and a new NuSTAR observation. NuSTAR data above 10 keV allowed
us to detect the 110-ms spin period of the pulsar, characterize its hard X-ray
pulse profile, and resolve hard X-ray emission from the PWN after removing
contamination from the pulsar and other overlapping point sources. The extended
PWN was detected up to 20 keV and is well described by a power-law model
with a photon index 2. The PWN shape does not vary significantly
with energy, and its X-ray spectrum shows no clear evidence of softening away
from the pulsar. We modeled the spatial profile of X-ray spectra and broadband
spectral energy distribution in the radio to TeV band to infer the physical
properties of the PWN. We found that a model with low magnetic field strength
( G) and efficient diffusion ( cm s)
fits the PWN data well. The extended hard X-ray and TeV emission, associated
respectively with synchrotron radiation and inverse Compton scattering by
relativistic electrons, suggests that particles are accelerated to very high
energies ( TeV), indicating that the Rabbit PWN is a Galactic
PeVatron candidate.Comment: 21 pages, 10 figures. ApJ accepte
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