8,870 research outputs found
Applying Deep Learning To Airbnb Search
The application to search ranking is one of the biggest machine learning
success stories at Airbnb. Much of the initial gains were driven by a gradient
boosted decision tree model. The gains, however, plateaued over time. This
paper discusses the work done in applying neural networks in an attempt to
break out of that plateau. We present our perspective not with the intention of
pushing the frontier of new modeling techniques. Instead, ours is a story of
the elements we found useful in applying neural networks to a real life
product. Deep learning was steep learning for us. To other teams embarking on
similar journeys, we hope an account of our struggles and triumphs will provide
some useful pointers. Bon voyage!Comment: 8 page
Efficient Nearest Neighbor Classification Using a Cascade of Approximate Similarity Measures
Nearest neighbor classification using shape context can yield highly accurate results in a number of recognition problems. Unfortunately, the approach can be too slow for practical applications, and thus approximation strategies are needed to make shape context practical. This paper proposes a method for efficient and accurate nearest neighbor classification in non-Euclidean spaces, such as the space induced by the shape context measure. First, a method is introduced for constructing a Euclidean embedding that is optimized for nearest neighbor classification accuracy. Using that embedding, multiple approximations of the underlying non-Euclidean similarity measure are obtained, at different levels of accuracy and efficiency. The approximations are automatically combined to form a cascade classifier, which applies the slower approximations only to the hardest cases. Unlike typical cascade-of-classifiers approaches, that are applied to binary classification problems, our method constructs a cascade for a multiclass problem. Experiments with a standard shape data set indicate that a two-to-three order of magnitude speed up is gained over the standard shape context classifier, with minimal losses in classification accuracy.National Science Foundation (IIS-0308213, IIS-0329009, EIA-0202067); Office of Naval Research (N00014-03-1-0108
HBST: A Hamming Distance embedding Binary Search Tree for Visual Place Recognition
Reliable and efficient Visual Place Recognition is a major building block of
modern SLAM systems. Leveraging on our prior work, in this paper we present a
Hamming Distance embedding Binary Search Tree (HBST) approach for binary
Descriptor Matching and Image Retrieval. HBST allows for descriptor Search and
Insertion in logarithmic time by exploiting particular properties of binary
Feature descriptors. We support the idea behind our search structure with a
thorough analysis on the exploited descriptor properties and their effects on
completeness and complexity of search and insertion. To validate our claims we
conducted comparative experiments for HBST and several state-of-the-art methods
on a broad range of publicly available datasets. HBST is available as a compact
open-source C++ header-only library.Comment: Submitted to IEEE Robotics and Automation Letters (RA-L) 2018 with
International Conference on Intelligent Robots and Systems (IROS) 2018
option, 8 pages, 10 figure
Using Deep Networks for Drone Detection
Drone detection is the problem of finding the smallest rectangle that
encloses the drone(s) in a video sequence. In this study, we propose a solution
using an end-to-end object detection model based on convolutional neural
networks. To solve the scarce data problem for training the network, we propose
an algorithm for creating an extensive artificial dataset by combining
background-subtracted real images. With this approach, we can achieve precision
and recall values both of which are high at the same time.Comment: To appear in International Workshop on Small-Drone Surveillance,
Detection and Counteraction Techniques organised within AVSS 201
Improving the Boosted Correlogram
Introduced seven years ago, the correlogram is a simple statistical image descriptor that nevertheless performs strongly on image retrieval tasks. As a result it has found wide use as a component inside larger systems for content-based image and video retrieval. Yet few studies have examined potential variants of the correlogram or compared their performance to the original. This paper presents systematic experiments on the correlogram and several variants under different conditions, showing that the results may vary significantly depending on both the variant chosen and its mode of application. As expected, the experimental setup combining correlogram variants with boosting shows the best results of those tested. Under these prime conditions, a novel variant of the correlogram shows a higher average precision for many image categories than the form commonly used
DarkRank: Accelerating Deep Metric Learning via Cross Sample Similarities Transfer
We have witnessed rapid evolution of deep neural network architecture design
in the past years. These latest progresses greatly facilitate the developments
in various areas such as computer vision and natural language processing.
However, along with the extraordinary performance, these state-of-the-art
models also bring in expensive computational cost. Directly deploying these
models into applications with real-time requirement is still infeasible.
Recently, Hinton etal. have shown that the dark knowledge within a powerful
teacher model can significantly help the training of a smaller and faster
student network. These knowledge are vastly beneficial to improve the
generalization ability of the student model. Inspired by their work, we
introduce a new type of knowledge -- cross sample similarities for model
compression and acceleration. This knowledge can be naturally derived from deep
metric learning model. To transfer them, we bring the "learning to rank"
technique into deep metric learning formulation. We test our proposed DarkRank
method on various metric learning tasks including pedestrian re-identification,
image retrieval and image clustering. The results are quite encouraging. Our
method can improve over the baseline method by a large margin. Moreover, it is
fully compatible with other existing methods. When combined, the performance
can be further boosted
- …