3,958 research outputs found
Unsupervised Network Pretraining via Encoding Human Design
Over the years, computer vision researchers have spent an immense amount of
effort on designing image features for the visual object recognition task. We
propose to incorporate this valuable experience to guide the task of training
deep neural networks. Our idea is to pretrain the network through the task of
replicating the process of hand-designed feature extraction. By learning to
replicate the process, the neural network integrates previous research
knowledge and learns to model visual objects in a way similar to the
hand-designed features. In the succeeding finetuning step, it further learns
object-specific representations from labeled data and this boosts its
classification power. We pretrain two convolutional neural networks where one
replicates the process of histogram of oriented gradients feature extraction,
and the other replicates the process of region covariance feature extraction.
After finetuning, we achieve substantially better performance than the baseline
methods.Comment: 9 pages, 11 figures, WACV 2016: IEEE Conference on Applications of
Computer Visio
Building high-level features using large scale unsupervised learning
We consider the problem of building high-level, class-specific feature
detectors from only unlabeled data. For example, is it possible to learn a face
detector using only unlabeled images? To answer this, we train a 9-layered
locally connected sparse autoencoder with pooling and local contrast
normalization on a large dataset of images (the model has 1 billion
connections, the dataset has 10 million 200x200 pixel images downloaded from
the Internet). We train this network using model parallelism and asynchronous
SGD on a cluster with 1,000 machines (16,000 cores) for three days. Contrary to
what appears to be a widely-held intuition, our experimental results reveal
that it is possible to train a face detector without having to label images as
containing a face or not. Control experiments show that this feature detector
is robust not only to translation but also to scaling and out-of-plane
rotation. We also find that the same network is sensitive to other high-level
concepts such as cat faces and human bodies. Starting with these learned
features, we trained our network to obtain 15.8% accuracy in recognizing 20,000
object categories from ImageNet, a leap of 70% relative improvement over the
previous state-of-the-art
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