Accelerated face detector training using the PSL framework

We train a face detection system using the PSL framework [1] which combines the AdaBoost learning algorithm and Haar-like features. We demonstrate the ability of this framework to overcome some of the challenges inherent in training classifiers that are structured in cascades of boosted ensembles (CoBE). The PSL classifiers are compared to the Viola-Jones type cas- caded classifiers. We establish the ability of the PSL framework to produce classifiers in a complex domain in significantly reduced time frame. They also comprise of fewer boosted en- sembles albeit at a price of increased false detection rates on our test dataset. We also report on results from a more diverse number of experiments carried out on the PSL framework in order to shed more insight into the effects of variations in its adjustable training parameters

Cascading Randomized Weighted Majority: A New Online Ensemble Learning Algorithm

With the increasing volume of data in the world, the best approach for learning from this data is to exploit an online learning algorithm. Online ensemble methods are online algorithms which take advantage of an ensemble of classifiers to predict labels of data. Prediction with expert advice is a well-studied problem in the online ensemble learning literature. The Weighted Majority algorithm and the randomized weighted majority (RWM) are the most well-known solutions to this problem, aiming to converge to the best expert. Since among some expert, the best one does not necessarily have the minimum error in all regions of data space, defining specific regions and converging to the best expert in each of these regions will lead to a better result. In this paper, we aim to resolve this defect of RWM algorithms by proposing a novel online ensemble algorithm to the problem of prediction with expert advice. We propose a cascading version of RWM to achieve not only better experimental results but also a better error bound for sufficiently large datasets.Comment: 15 pages, 3 figure

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

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

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