105 research outputs found
PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning
This paper presents a method for adding multiple tasks to a single deep
neural network while avoiding catastrophic forgetting. Inspired by network
pruning techniques, we exploit redundancies in large deep networks to free up
parameters that can then be employed to learn new tasks. By performing
iterative pruning and network re-training, we are able to sequentially "pack"
multiple tasks into a single network while ensuring minimal drop in performance
and minimal storage overhead. Unlike prior work that uses proxy losses to
maintain accuracy on older tasks, we always optimize for the task at hand. We
perform extensive experiments on a variety of network architectures and
large-scale datasets, and observe much better robustness against catastrophic
forgetting than prior work. In particular, we are able to add three
fine-grained classification tasks to a single ImageNet-trained VGG-16 network
and achieve accuracies close to those of separately trained networks for each
task. Code available at https://github.com/arunmallya/packne
Local, Semi-Local and Global Models for Texture, Object and Scene Recognition
This dissertation addresses the problems of recognizing textures, objects, and scenes in photographs. We present approaches to these recognition tasks that combine salient local image features with spatial relations and effective discriminative learning techniques. First, we introduce a bag of features image model for recognizing textured surfaces under a wide range of transformations, including viewpoint changes and non-rigid deformations. We present results of a large-scale comparative evaluation indicating that bags of features can be effective not only for texture, but also for object categization, even in the presence of substantial clutter and intra-class variation. We also show how to augment the purely local image representation with statistical co-occurrence relations between pairs of nearby features, and develop a learning and classification framework for the task of classifying individual features in a multi-texture image. Next, we present a more structured alternative to bags of features for object recognition, namely, an image representation based on semi-local parts, or groups of features characterized by stable appearance and geometric layout. Semi-local parts are automatically learned from small sets of unsegmented, cluttered images. Finally, we present a global method for recognizing scene categories that works by partitioning the image into increasingly fine sub-regions and computing histograms of local features found inside each sub-region. The resulting spatial pyramid representation demonstrates significantly improved performance on challenging scene categorization tasks
Adaptive Object Detection Using Adjacency and Zoom Prediction
State-of-the-art object detection systems rely on an accurate set of region
proposals. Several recent methods use a neural network architecture to
hypothesize promising object locations. While these approaches are
computationally efficient, they rely on fixed image regions as anchors for
predictions. In this paper we propose to use a search strategy that adaptively
directs computational resources to sub-regions likely to contain objects.
Compared to methods based on fixed anchor locations, our approach naturally
adapts to cases where object instances are sparse and small. Our approach is
comparable in terms of accuracy to the state-of-the-art Faster R-CNN approach
while using two orders of magnitude fewer anchors on average. Code is publicly
available.Comment: Accepted to CVPR 201
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