103,269 research outputs found
LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop
While there has been remarkable progress in the performance of visual
recognition algorithms, the state-of-the-art models tend to be exceptionally
data-hungry. Large labeled training datasets, expensive and tedious to produce,
are required to optimize millions of parameters in deep network models. Lagging
behind the growth in model capacity, the available datasets are quickly
becoming outdated in terms of size and density. To circumvent this bottleneck,
we propose to amplify human effort through a partially automated labeling
scheme, leveraging deep learning with humans in the loop. Starting from a large
set of candidate images for each category, we iteratively sample a subset, ask
people to label them, classify the others with a trained model, split the set
into positives, negatives, and unlabeled based on the classification
confidence, and then iterate with the unlabeled set. To assess the
effectiveness of this cascading procedure and enable further progress in visual
recognition research, we construct a new image dataset, LSUN. It contains
around one million labeled images for each of 10 scene categories and 20 object
categories. We experiment with training popular convolutional networks and find
that they achieve substantial performance gains when trained on this dataset
The Devil of Face Recognition is in the Noise
The growing scale of face recognition datasets empowers us to train strong
convolutional networks for face recognition. While a variety of architectures
and loss functions have been devised, we still have a limited understanding of
the source and consequence of label noise inherent in existing datasets. We
make the following contributions: 1) We contribute cleaned subsets of popular
face databases, i.e., MegaFace and MS-Celeb-1M datasets, and build a new
large-scale noise-controlled IMDb-Face dataset. 2) With the original datasets
and cleaned subsets, we profile and analyze label noise properties of MegaFace
and MS-Celeb-1M. We show that a few orders more samples are needed to achieve
the same accuracy yielded by a clean subset. 3) We study the association
between different types of noise, i.e., label flips and outliers, with the
accuracy of face recognition models. 4) We investigate ways to improve data
cleanliness, including a comprehensive user study on the influence of data
labeling strategies to annotation accuracy. The IMDb-Face dataset has been
released on https://github.com/fwang91/IMDb-Face.Comment: accepted to ECCV'1
PANDA: Pose Aligned Networks for Deep Attribute Modeling
We propose a method for inferring human attributes (such as gender, hair
style, clothes style, expression, action) from images of people under large
variation of viewpoint, pose, appearance, articulation and occlusion.
Convolutional Neural Nets (CNN) have been shown to perform very well on large
scale object recognition problems. In the context of attribute classification,
however, the signal is often subtle and it may cover only a small part of the
image, while the image is dominated by the effects of pose and viewpoint.
Discounting for pose variation would require training on very large labeled
datasets which are not presently available. Part-based models, such as poselets
and DPM have been shown to perform well for this problem but they are limited
by shallow low-level features. We propose a new method which combines
part-based models and deep learning by training pose-normalized CNNs. We show
substantial improvement vs. state-of-the-art methods on challenging attribute
classification tasks in unconstrained settings. Experiments confirm that our
method outperforms both the best part-based methods on this problem and
conventional CNNs trained on the full bounding box of the person.Comment: 8 page
Webly Supervised Learning of Convolutional Networks
We present an approach to utilize large amounts of web data for learning
CNNs. Specifically inspired by curriculum learning, we present a two-step
approach for CNN training. First, we use easy images to train an initial visual
representation. We then use this initial CNN and adapt it to harder, more
realistic images by leveraging the structure of data and categories. We
demonstrate that our two-stage CNN outperforms a fine-tuned CNN trained on
ImageNet on Pascal VOC 2012. We also demonstrate the strength of webly
supervised learning by localizing objects in web images and training a R-CNN
style detector. It achieves the best performance on VOC 2007 where no VOC
training data is used. Finally, we show our approach is quite robust to noise
and performs comparably even when we use image search results from March 2013
(pre-CNN image search era)
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