3,986 research outputs found
Unsupervised Neural Machine Translation with SMT as Posterior Regularization
Without real bilingual corpus available, unsupervised Neural Machine
Translation (NMT) typically requires pseudo parallel data generated with the
back-translation method for the model training. However, due to weak
supervision, the pseudo data inevitably contain noises and errors that will be
accumulated and reinforced in the subsequent training process, leading to bad
translation performance. To address this issue, we introduce phrase based
Statistic Machine Translation (SMT) models which are robust to noisy data, as
posterior regularizations to guide the training of unsupervised NMT models in
the iterative back-translation process. Our method starts from SMT models built
with pre-trained language models and word-level translation tables inferred
from cross-lingual embeddings. Then SMT and NMT models are optimized jointly
and boost each other incrementally in a unified EM framework. In this way, (1)
the negative effect caused by errors in the iterative back-translation process
can be alleviated timely by SMT filtering noises from its phrase tables;
meanwhile, (2) NMT can compensate for the deficiency of fluency inherent in
SMT. Experiments conducted on en-fr and en-de translation tasks show that our
method outperforms the strong baseline and achieves new state-of-the-art
unsupervised machine translation performance.Comment: To be presented at AAAI 2019; 9 pages, 4 figure
Conditional Random Field Autoencoders for Unsupervised Structured Prediction
We introduce a framework for unsupervised learning of structured predictors
with overlapping, global features. Each input's latent representation is
predicted conditional on the observable data using a feature-rich conditional
random field. Then a reconstruction of the input is (re)generated, conditional
on the latent structure, using models for which maximum likelihood estimation
has a closed-form. Our autoencoder formulation enables efficient learning
without making unrealistic independence assumptions or restricting the kinds of
features that can be used. We illustrate insightful connections to traditional
autoencoders, posterior regularization and multi-view learning. We show
competitive results with instantiations of the model for two canonical NLP
tasks: part-of-speech induction and bitext word alignment, and show that
training our model can be substantially more efficient than comparable
feature-rich baselines
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
Unsupervised Adversarial Depth Estimation using Cycled Generative Networks
While recent deep monocular depth estimation approaches based on supervised
regression have achieved remarkable performance, costly ground truth
annotations are required during training. To cope with this issue, in this
paper we present a novel unsupervised deep learning approach for predicting
depth maps and show that the depth estimation task can be effectively tackled
within an adversarial learning framework. Specifically, we propose a deep
generative network that learns to predict the correspondence field i.e. the
disparity map between two image views in a calibrated stereo camera setting.
The proposed architecture consists of two generative sub-networks jointly
trained with adversarial learning for reconstructing the disparity map and
organized in a cycle such as to provide mutual constraints and supervision to
each other. Extensive experiments on the publicly available datasets KITTI and
Cityscapes demonstrate the effectiveness of the proposed model and competitive
results with state of the art methods. The code and trained model are available
on https://github.com/andrea-pilzer/unsup-stereo-depthGAN.Comment: To appear in 3DV 2018. Code is available on GitHu
Unsupervised feature learning by augmenting single images
When deep learning is applied to visual object recognition, data augmentation
is often used to generate additional training data without extra labeling cost.
It helps to reduce overfitting and increase the performance of the algorithm.
In this paper we investigate if it is possible to use data augmentation as the
main component of an unsupervised feature learning architecture. To that end we
sample a set of random image patches and declare each of them to be a separate
single-image surrogate class. We then extend these trivial one-element classes
by applying a variety of transformations to the initial 'seed' patches. Finally
we train a convolutional neural network to discriminate between these surrogate
classes. The feature representation learned by the network can then be used in
various vision tasks. We find that this simple feature learning algorithm is
surprisingly successful, achieving competitive classification results on
several popular vision datasets (STL-10, CIFAR-10, Caltech-101).Comment: ICLR 2014 workshop track submission (7 pages, 4 figures, 1 table
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