44,115 research outputs found
Dense prediction of label noise for learning building extraction from aerial drone imagery
Label noise is a commonly encountered problem in learning building extraction tasks; its presence can reduce performance and increase learning complexity. This is especially true for cases where high resolution aerial drone imagery is used, as the labels may not perfectly correspond/align with the actual objects in the imagery. In general machine learning and computer vision context, labels refer to the associated class of data, and in remote sensing-based building extraction refer to pixel-level classes. Dense label noise in building extraction tasks has rarely been formalized and assessed. We formulate a taxonomy of label noise models for building extraction tasks, which incorporates both pixel-wise and dense models. While learning dense prediction under label noise, the differences between the ground truth clean label and observed noisy label can be encoded by error matrices indicating locations and type of noisy pixel-level labels. In this work, we explicitly learn to approximate error matrices for improving building extraction performance; essentially, learning dense prediction of label noise as a subtask of a larger building extraction task. We propose two new model frameworks for learning building extraction under dense real-world label noise, and consequently two new network architectures, which approximate the error matrices as intermediate predictions. The first model learns the general error matrix as an intermediate step and the second model learns the false positive and false-negative error matrices independently, as intermediate steps. Approximating intermediate error matrices can generate label noise saliency maps, for identifying labels having higher chances of being mis-labelled. We have used ultra-high-resolution aerial images, noisy observed labels from OpenStreetMap, and clean labels obtained after careful annotation by the authors. When compared to the baseline model trained and tested using clean labels, our intermediate false positive-false negative error matrix model provides Intersection-Over-Union gain of 2.74% and F1-score gain of 1.75% on the independent test set. Furthermore, our proposed models provide much higher recall than currently used deep learning models for building extraction, while providing comparable precision. We show that intermediate false positive-false negative error matrix approximation can improve performance under label noise
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
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
Robust Loss Functions under Label Noise for Deep Neural Networks
In many applications of classifier learning, training data suffers from label
noise. Deep networks are learned using huge training data where the problem of
noisy labels is particularly relevant. The current techniques proposed for
learning deep networks under label noise focus on modifying the network
architecture and on algorithms for estimating true labels from noisy labels. An
alternate approach would be to look for loss functions that are inherently
noise-tolerant. For binary classification there exist theoretical results on
loss functions that are robust to label noise. In this paper, we provide some
sufficient conditions on a loss function so that risk minimization under that
loss function would be inherently tolerant to label noise for multiclass
classification problems. These results generalize the existing results on
noise-tolerant loss functions for binary classification. We study some of the
widely used loss functions in deep networks and show that the loss function
based on mean absolute value of error is inherently robust to label noise. Thus
standard back propagation is enough to learn the true classifier even under
label noise. Through experiments, we illustrate the robustness of risk
minimization with such loss functions for learning neural networks.Comment: Appeared in AAAI 201
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