28,924 research outputs found
Unsupervised feature learning with discriminative encoder
In recent years, deep discriminative models have achieved extraordinary
performance on supervised learning tasks, significantly outperforming their
generative counterparts. However, their success relies on the presence of a
large amount of labeled data. How can one use the same discriminative models
for learning useful features in the absence of labels? We address this question
in this paper, by jointly modeling the distribution of data and latent features
in a manner that explicitly assigns zero probability to unobserved data. Rather
than maximizing the marginal probability of observed data, we maximize the
joint probability of the data and the latent features using a two step EM-like
procedure. To prevent the model from overfitting to our initial selection of
latent features, we use adversarial regularization. Depending on the task, we
allow the latent features to be one-hot or real-valued vectors and define a
suitable prior on the features. For instance, one-hot features correspond to
class labels and are directly used for the unsupervised and semi-supervised
classification task, whereas real-valued feature vectors are fed as input to
simple classifiers for auxiliary supervised discrimination tasks. The proposed
model, which we dub discriminative encoder (or DisCoder), is flexible in the
type of latent features that it can capture. The proposed model achieves
state-of-the-art performance on several challenging tasks.Comment: 10 pages, 4 figures, International Conference on Data Mining, 201
Are Accuracy and Robustness Correlated?
Machine learning models are vulnerable to adversarial examples formed by
applying small carefully chosen perturbations to inputs that cause unexpected
classification errors. In this paper, we perform experiments on various
adversarial example generation approaches with multiple deep convolutional
neural networks including Residual Networks, the best performing models on
ImageNet Large-Scale Visual Recognition Challenge 2015. We compare the
adversarial example generation techniques with respect to the quality of the
produced images, and measure the robustness of the tested machine learning
models to adversarial examples. Finally, we conduct large-scale experiments on
cross-model adversarial portability. We find that adversarial examples are
mostly transferable across similar network topologies, and we demonstrate that
better machine learning models are less vulnerable to adversarial examples.Comment: Accepted for publication at ICMLA 201
Cycle-Consistent Deep Generative Hashing for Cross-Modal Retrieval
In this paper, we propose a novel deep generative approach to cross-modal
retrieval to learn hash functions in the absence of paired training samples
through the cycle consistency loss. Our proposed approach employs adversarial
training scheme to lean a couple of hash functions enabling translation between
modalities while assuming the underlying semantic relationship. To induce the
hash codes with semantics to the input-output pair, cycle consistency loss is
further proposed upon the adversarial training to strengthen the correlations
between inputs and corresponding outputs. Our approach is generative to learn
hash functions such that the learned hash codes can maximally correlate each
input-output correspondence, meanwhile can also regenerate the inputs so as to
minimize the information loss. The learning to hash embedding is thus performed
to jointly optimize the parameters of the hash functions across modalities as
well as the associated generative models. Extensive experiments on a variety of
large-scale cross-modal data sets demonstrate that our proposed method achieves
better retrieval results than the state-of-the-arts.Comment: To appeared on IEEE Trans. Image Processing. arXiv admin note: text
overlap with arXiv:1703.10593 by other author
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