3,581 research outputs found
A Semi-Supervised Two-Stage Approach to Learning from Noisy Labels
The recent success of deep neural networks is powered in part by large-scale
well-labeled training data. However, it is a daunting task to laboriously
annotate an ImageNet-like dateset. On the contrary, it is fairly convenient,
fast, and cheap to collect training images from the Web along with their noisy
labels. This signifies the need of alternative approaches to training deep
neural networks using such noisy labels. Existing methods tackling this problem
either try to identify and correct the wrong labels or reweigh the data terms
in the loss function according to the inferred noisy rates. Both strategies
inevitably incur errors for some of the data points. In this paper, we contend
that it is actually better to ignore the labels of some of the data points than
to keep them if the labels are incorrect, especially when the noisy rate is
high. After all, the wrong labels could mislead a neural network to a bad local
optimum. We suggest a two-stage framework for the learning from noisy labels.
In the first stage, we identify a small portion of images from the noisy
training set of which the labels are correct with a high probability. The noisy
labels of the other images are ignored. In the second stage, we train a deep
neural network in a semi-supervised manner. This framework effectively takes
advantage of the whole training set and yet only a portion of its labels that
are most likely correct. Experiments on three datasets verify the effectiveness
of our approach especially when the noisy rate is high
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)
AffinityNet: semi-supervised few-shot learning for disease type prediction
While deep learning has achieved great success in computer vision and many
other fields, currently it does not work very well on patient genomic data with
the "big p, small N" problem (i.e., a relatively small number of samples with
high-dimensional features). In order to make deep learning work with a small
amount of training data, we have to design new models that facilitate few-shot
learning. Here we present the Affinity Network Model (AffinityNet), a data
efficient deep learning model that can learn from a limited number of training
examples and generalize well. The backbone of the AffinityNet model consists of
stacked k-Nearest-Neighbor (kNN) attention pooling layers. The kNN attention
pooling layer is a generalization of the Graph Attention Model (GAM), and can
be applied to not only graphs but also any set of objects regardless of whether
a graph is given or not. As a new deep learning module, kNN attention pooling
layers can be plugged into any neural network model just like convolutional
layers. As a simple special case of kNN attention pooling layer, feature
attention layer can directly select important features that are useful for
classification tasks. Experiments on both synthetic data and cancer genomic
data from TCGA projects show that our AffinityNet model has better
generalization power than conventional neural network models with little
training data. The code is freely available at
https://github.com/BeautyOfWeb/AffinityNet .Comment: 14 pages, 6 figure
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