4,454 research outputs found
Do Deep Generative Models Know What They Don't Know?
A neural network deployed in the wild may be asked to make predictions for
inputs that were drawn from a different distribution than that of the training
data. A plethora of work has demonstrated that it is easy to find or synthesize
inputs for which a neural network is highly confident yet wrong. Generative
models are widely viewed to be robust to such mistaken confidence as modeling
the density of the input features can be used to detect novel,
out-of-distribution inputs. In this paper we challenge this assumption. We find
that the density learned by flow-based models, VAEs, and PixelCNNs cannot
distinguish images of common objects such as dogs, trucks, and horses (i.e.
CIFAR-10) from those of house numbers (i.e. SVHN), assigning a higher
likelihood to the latter when the model is trained on the former. Moreover, we
find evidence of this phenomenon when pairing several popular image data sets:
FashionMNIST vs MNIST, CelebA vs SVHN, ImageNet vs CIFAR-10 / CIFAR-100 / SVHN.
To investigate this curious behavior, we focus analysis on flow-based
generative models in particular since they are trained and evaluated via the
exact marginal likelihood. We find such behavior persists even when we restrict
the flows to constant-volume transformations. These transformations admit some
theoretical analysis, and we show that the difference in likelihoods can be
explained by the location and variances of the data and the model curvature.
Our results caution against using the density estimates from deep generative
models to identify inputs similar to the training distribution until their
behavior for out-of-distribution inputs is better understood.Comment: ICLR 201
Autoencoders and Generative Adversarial Networks for Imbalanced Sequence Classification
Generative Adversarial Networks (GANs) have been used in many different
applications to generate realistic synthetic data. We introduce a novel GAN
with Autoencoder (GAN-AE) architecture to generate synthetic samples for
variable length, multi-feature sequence datasets. In this model, we develop a
GAN architecture with an additional autoencoder component, where recurrent
neural networks (RNNs) are used for each component of the model in order to
generate synthetic data to improve classification accuracy for a highly
imbalanced medical device dataset. In addition to the medical device dataset,
we also evaluate the GAN-AE performance on two additional datasets and
demonstrate the application of GAN-AE to a sequence-to-sequence task where both
synthetic sequence inputs and sequence outputs must be generated. To evaluate
the quality of the synthetic data, we train encoder-decoder models both with
and without the synthetic data and compare the classification model
performance. We show that a model trained with GAN-AE generated synthetic data
outperforms models trained with synthetic data generated both with standard
oversampling techniques such as SMOTE and Autoencoders as well as with state of
the art GAN-based models
Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective
This paper takes a problem-oriented perspective and presents a comprehensive
review of transfer learning methods, both shallow and deep, for cross-dataset
visual recognition. Specifically, it categorises the cross-dataset recognition
into seventeen problems based on a set of carefully chosen data and label
attributes. Such a problem-oriented taxonomy has allowed us to examine how
different transfer learning approaches tackle each problem and how well each
problem has been researched to date. The comprehensive problem-oriented review
of the advances in transfer learning with respect to the problem has not only
revealed the challenges in transfer learning for visual recognition, but also
the problems (e.g. eight of the seventeen problems) that have been scarcely
studied. This survey not only presents an up-to-date technical review for
researchers, but also a systematic approach and a reference for a machine
learning practitioner to categorise a real problem and to look up for a
possible solution accordingly
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