3,265 research outputs found
Sacrificing Accuracy for Reduced Computation: Cascaded Inference Based on Softmax Confidence
We study the tradeoff between computational effort and accuracy in a cascade
of deep neural networks. During inference, early termination in the cascade is
controlled by confidence levels derived directly from the softmax outputs of
intermediate classifiers. The advantage of early termination is that
classification is performed using less computation, thus adjusting the
computational effort to the complexity of the input. Moreover, dynamic
modification of confidence thresholds allow one to trade accuracy for
computational effort without requiring retraining. Basing of early termination
on softmax classifier outputs is justified by experimentation that demonstrates
an almost linear relation between confidence levels in intermediate classifiers
and accuracy. Our experimentation with architectures based on ResNet obtained
the following results. (i) A speedup of 1.5 that sacrifices 1.4% accuracy with
respect to the CIFAR-10 test set. (ii) A speedup of 1.19 that sacrifices 0.7%
accuracy with respect to the CIFAR-100 test set. (iii) A speedup of 2.16 that
sacrifices 1.4% accuracy with respect to the SVHN test set
Fast Video Classification via Adaptive Cascading of Deep Models
Recent advances have enabled "oracle" classifiers that can classify across
many classes and input distributions with high accuracy without retraining.
However, these classifiers are relatively heavyweight, so that applying them to
classify video is costly. We show that day-to-day video exhibits highly skewed
class distributions over the short term, and that these distributions can be
classified by much simpler models. We formulate the problem of detecting the
short-term skews online and exploiting models based on it as a new sequential
decision making problem dubbed the Online Bandit Problem, and present a new
algorithm to solve it. When applied to recognizing faces in TV shows and
movies, we realize end-to-end classification speedups of 2.4-7.8x/2.6-11.2x (on
GPU/CPU) relative to a state-of-the-art convolutional neural network, at
competitive accuracy.Comment: Accepted at IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), 201
Adaptive Target Recognition: A Case Study Involving Airport Baggage Screening
This work addresses the question whether it is possible to design a
computer-vision based automatic threat recognition (ATR) system so that it can
adapt to changing specifications of a threat without having to create a new ATR
each time. The changes in threat specifications, which may be warranted by
intelligence reports and world events, are typically regarding the physical
characteristics of what constitutes a threat: its material composition, its
shape, its method of concealment, etc. Here we present our design of an AATR
system (Adaptive ATR) that can adapt to changing specifications in materials
characterization (meaning density, as measured by its x-ray attenuation
coefficient), its mass, and its thickness. Our design uses a two-stage cascaded
approach, in which the first stage is characterized by a high recall rate over
the entire range of possibilities for the threat parameters that are allowed to
change. The purpose of the second stage is to then fine-tune the performance of
the overall system for the current threat specifications. The computational
effort for this fine-tuning for achieving a desired PD/PFA rate is far less
than what it would take to create a new classifier with the same overall
performance for the new set of threat specifications
Boosted Cascaded Convnets for Multilabel Classification of Thoracic Diseases in Chest Radiographs
Chest X-ray is one of the most accessible medical imaging technique for
diagnosis of multiple diseases. With the availability of ChestX-ray14, which is
a massive dataset of chest X-ray images and provides annotations for 14
thoracic diseases; it is possible to train Deep Convolutional Neural Networks
(DCNN) to build Computer Aided Diagnosis (CAD) systems. In this work, we
experiment a set of deep learning models and present a cascaded deep neural
network that can diagnose all 14 pathologies better than the baseline and is
competitive with other published methods. Our work provides the quantitative
results to answer following research questions for the dataset: 1) What loss
functions to use for training DCNN from scratch on ChestX-ray14 dataset that
demonstrates high class imbalance and label co occurrence? 2) How to use
cascading to model label dependency and to improve accuracy of the deep
learning model?Comment: Submitted to CVPR 201
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