11,656 research outputs found
CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise
In this paper, we study the problem of learning image classification models
with label noise. Existing approaches depending on human supervision are
generally not scalable as manually identifying correct or incorrect labels is
time-consuming, whereas approaches not relying on human supervision are
scalable but less effective. To reduce the amount of human supervision for
label noise cleaning, we introduce CleanNet, a joint neural embedding network,
which only requires a fraction of the classes being manually verified to
provide the knowledge of label noise that can be transferred to other classes.
We further integrate CleanNet and conventional convolutional neural network
classifier into one framework for image classification learning. We demonstrate
the effectiveness of the proposed algorithm on both of the label noise
detection task and the image classification on noisy data task on several
large-scale datasets. Experimental results show that CleanNet can reduce label
noise detection error rate on held-out classes where no human supervision
available by 41.5% compared to current weakly supervised methods. It also
achieves 47% of the performance gain of verifying all images with only 3.2%
images verified on an image classification task. Source code and dataset will
be available at kuanghuei.github.io/CleanNetProject.Comment: Accepted to CVPR 201
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
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