26,691 research outputs found
ImageNet Large Scale Visual Recognition Challenge
The ImageNet Large Scale Visual Recognition Challenge is a benchmark in
object category classification and detection on hundreds of object categories
and millions of images. The challenge has been run annually from 2010 to
present, attracting participation from more than fifty institutions.
This paper describes the creation of this benchmark dataset and the advances
in object recognition that have been possible as a result. We discuss the
challenges of collecting large-scale ground truth annotation, highlight key
breakthroughs in categorical object recognition, provide a detailed analysis of
the current state of the field of large-scale image classification and object
detection, and compare the state-of-the-art computer vision accuracy with human
accuracy. We conclude with lessons learned in the five years of the challenge,
and propose future directions and improvements.Comment: 43 pages, 16 figures. v3 includes additional comparisons with PASCAL
VOC (per-category comparisons in Table 3, distribution of localization
difficulty in Fig 16), a list of queries used for obtaining object detection
images (Appendix C), and some additional reference
Are Accuracy and Robustness Correlated?
Machine learning models are vulnerable to adversarial examples formed by
applying small carefully chosen perturbations to inputs that cause unexpected
classification errors. In this paper, we perform experiments on various
adversarial example generation approaches with multiple deep convolutional
neural networks including Residual Networks, the best performing models on
ImageNet Large-Scale Visual Recognition Challenge 2015. We compare the
adversarial example generation techniques with respect to the quality of the
produced images, and measure the robustness of the tested machine learning
models to adversarial examples. Finally, we conduct large-scale experiments on
cross-model adversarial portability. We find that adversarial examples are
mostly transferable across similar network topologies, and we demonstrate that
better machine learning models are less vulnerable to adversarial examples.Comment: Accepted for publication at ICMLA 201
Large-scale image classification using ensembles of nested dichotomies
Many techniques to reduce the cost at test time in large-scale problems involve a hierarchical organization of classifiers, but are either too expensive to learn or degrade the classification performance. Conversely, in this work we show that using ensembles of randomized hierarchical decompositions of the original problem can both improve the accuracy and reduce the computational complexity at test time. The proposed method is evaluated in the ImageNet Large Scale Visual Recognition Challenge’10, with promising results.Peer ReviewedPostprint (author’s final draft
Going Deeper with Convolutions
We propose a deep convolutional neural network architecture codenamed
"Inception", which was responsible for setting the new state of the art for
classification and detection in the ImageNet Large-Scale Visual Recognition
Challenge 2014 (ILSVRC 2014). The main hallmark of this architecture is the
improved utilization of the computing resources inside the network. This was
achieved by a carefully crafted design that allows for increasing the depth and
width of the network while keeping the computational budget constant. To
optimize quality, the architectural decisions were based on the Hebbian
principle and the intuition of multi-scale processing. One particular
incarnation used in our submission for ILSVRC 2014 is called GoogLeNet, a 22
layers deep network, the quality of which is assessed in the context of
classification and detection
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