766 research outputs found
To go deep or wide in learning?
To achieve acceptable performance for AI tasks, one can either use
sophisticated feature extraction methods as the first layer in a two-layered
supervised learning model, or learn the features directly using a deep
(multi-layered) model. While the first approach is very problem-specific, the
second approach has computational overheads in learning multiple layers and
fine-tuning of the model. In this paper, we propose an approach called wide
learning based on arc-cosine kernels, that learns a single layer of infinite
width. We propose exact and inexact learning strategies for wide learning and
show that wide learning with single layer outperforms single layer as well as
deep architectures of finite width for some benchmark datasets.Comment: 9 pages, 1 figure, Accepted for publication in Seventeenth
International Conference on Artificial Intelligence and Statistic
Learning to segment with image-level supervision
Deep convolutional networks have achieved the state-of-the-art for semantic
image segmentation tasks. However, training these networks requires access to
densely labeled images, which are known to be very expensive to obtain. On the
other hand, the web provides an almost unlimited source of images annotated at
the image level. How can one utilize this much larger weakly annotated set for
tasks that require dense labeling? Prior work often relied on localization
cues, such as saliency maps, objectness priors, bounding boxes etc., to address
this challenging problem. In this paper, we propose a model that generates
auxiliary labels for each image, while simultaneously forcing the output of the
CNN to satisfy the mean-field constraints imposed by a conditional random
field. We show that one can enforce the CRF constraints by forcing the
distribution at each pixel to be close to the distribution of its neighbors.
This is in stark contrast with methods that compute a recursive expansion of
the mean-field distribution using a recurrent architecture and train the
resultant distribution. Instead, the proposed model adds an extra loss term to
the output of the CNN, and hence, is faster than recursive implementations. We
achieve the state-of-the-art for weakly supervised semantic image segmentation
on VOC 2012 dataset, assuming no manually labeled pixel level information is
available. Furthermore, the incorporation of conditional random fields in CNN
incurs little extra time during training.Comment: Published in WACV 201
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