10,193 research outputs found
Unsupervised label noise modeling and loss correction
Despite being robust to small amounts of label noise, convolutional neural networks trained with stochastic gradient methods have been shown to easily fit random labels. When there are a mixture of correct and mislabelled targets, networks
tend to fit the former before the latter. This suggests using a suitable two-component mixture model as an unsupervised generative model of sample loss values during training to allow online estimation of the probability that a sample is mislabelled. Specifically, we propose a beta mixture to estimate this probability and correct the loss by relying on the network prediction (the so-called bootstrapping loss). We further adapt mixup augmentation to drive our approach a step further. Experiments on CIFAR-10/100 and TinyImageNet demonstrate a robustness to label noise that substantially outperforms recent state-of-the-art. Source code is available at https://git.io/fjsvE and Appendix at https://arxiv.org/abs/1904.11238
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
Topic modeling-based domain adaptation for system combination
This paper gives the system description of the domain adaptation team of Dublin City University for our participation in the system combination task in the Second Workshop on Applying Machine Learning Techniques to Optimise the Division of Labour in Hybrid MT (ML4HMT-12). We used the results of unsupervised document classification as meta information to the system combination module. For the Spanish-English data, our strategy achieved 26.33 BLEU points, 0.33 BLEU points absolute improvement over the standard confusion-network-based system combination. This was the best score in terms of BLEU among six participants in ML4HMT-12
Fidelity-Weighted Learning
Training deep neural networks requires many training samples, but in practice
training labels are expensive to obtain and may be of varying quality, as some
may be from trusted expert labelers while others might be from heuristics or
other sources of weak supervision such as crowd-sourcing. This creates a
fundamental quality versus-quantity trade-off in the learning process. Do we
learn from the small amount of high-quality data or the potentially large
amount of weakly-labeled data? We argue that if the learner could somehow know
and take the label-quality into account when learning the data representation,
we could get the best of both worlds. To this end, we propose
"fidelity-weighted learning" (FWL), a semi-supervised student-teacher approach
for training deep neural networks using weakly-labeled data. FWL modulates the
parameter updates to a student network (trained on the task we care about) on a
per-sample basis according to the posterior confidence of its label-quality
estimated by a teacher (who has access to the high-quality labels). Both
student and teacher are learned from the data. We evaluate FWL on two tasks in
information retrieval and natural language processing where we outperform
state-of-the-art alternative semi-supervised methods, indicating that our
approach makes better use of strong and weak labels, and leads to better
task-dependent data representations.Comment: Published as a conference paper at ICLR 201
Wasserstein Introspective Neural Networks
We present Wasserstein introspective neural networks (WINN) that are both a
generator and a discriminator within a single model. WINN provides a
significant improvement over the recent introspective neural networks (INN)
method by enhancing INN's generative modeling capability. WINN has three
interesting properties: (1) A mathematical connection between the formulation
of the INN algorithm and that of Wasserstein generative adversarial networks
(WGAN) is made. (2) The explicit adoption of the Wasserstein distance into INN
results in a large enhancement to INN, achieving compelling results even with a
single classifier --- e.g., providing nearly a 20 times reduction in model size
over INN for unsupervised generative modeling. (3) When applied to supervised
classification, WINN also gives rise to improved robustness against adversarial
examples in terms of the error reduction. In the experiments, we report
encouraging results on unsupervised learning problems including texture, face,
and object modeling, as well as a supervised classification task against
adversarial attacks.Comment: Accepted to CVPR 2018 (Oral
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