2,639 research outputs found
Residual Parameter Transfer for Deep Domain Adaptation
The goal of Deep Domain Adaptation is to make it possible to use Deep Nets
trained in one domain where there is enough annotated training data in another
where there is little or none. Most current approaches have focused on learning
feature representations that are invariant to the changes that occur when going
from one domain to the other, which means using the same network parameters in
both domains. While some recent algorithms explicitly model the changes by
adapting the network parameters, they either severely restrict the possible
domain changes, or significantly increase the number of model parameters.
By contrast, we introduce a network architecture that includes auxiliary
residual networks, which we train to predict the parameters in the domain with
little annotated data from those in the other one. This architecture enables us
to flexibly preserve the similarities between domains where they exist and
model the differences when necessary. We demonstrate that our approach yields
higher accuracy than state-of-the-art methods without undue complexity
Multi-task additive models with shared transfer functions based on dictionary learning
Additive models form a widely popular class of regression models which
represent the relation between covariates and response variables as the sum of
low-dimensional transfer functions. Besides flexibility and accuracy, a key
benefit of these models is their interpretability: the transfer functions
provide visual means for inspecting the models and identifying domain-specific
relations between inputs and outputs. However, in large-scale problems
involving the prediction of many related tasks, learning independently additive
models results in a loss of model interpretability, and can cause overfitting
when training data is scarce. We introduce a novel multi-task learning approach
which provides a corpus of accurate and interpretable additive models for a
large number of related forecasting tasks. Our key idea is to share transfer
functions across models in order to reduce the model complexity and ease the
exploration of the corpus. We establish a connection with sparse dictionary
learning and propose a new efficient fitting algorithm which alternates between
sparse coding and transfer function updates. The former step is solved via an
extension of Orthogonal Matching Pursuit, whose properties are analyzed using a
novel recovery condition which extends existing results in the literature. The
latter step is addressed using a traditional dictionary update rule.
Experiments on real-world data demonstrate that our approach compares favorably
to baseline methods while yielding an interpretable corpus of models, revealing
structure among the individual tasks and being more robust when training data
is scarce. Our framework therefore extends the well-known benefits of additive
models to common regression settings possibly involving thousands of tasks
Learning to Reconstruct Texture-less Deformable Surfaces from a Single View
Recent years have seen the development of mature solutions for reconstructing
deformable surfaces from a single image, provided that they are relatively
well-textured. By contrast, recovering the 3D shape of texture-less surfaces
remains an open problem, and essentially relates to Shape-from-Shading. In this
paper, we introduce a data-driven approach to this problem. We introduce a
general framework that can predict diverse 3D representations, such as meshes,
normals, and depth maps. Our experiments show that meshes are ill-suited to
handle texture-less 3D reconstruction in our context. Furthermore, we
demonstrate that our approach generalizes well to unseen objects, and that it
yields higher-quality reconstructions than a state-of-the-art SfS technique,
particularly in terms of normal estimates. Our reconstructions accurately model
the fine details of the surfaces, such as the creases of a T-Shirt worn by a
person.Comment: Accepted to 3DV 201
Detecting the Unexpected via Image Resynthesis
Classical semantic segmentation methods, including the recent deep learning
ones, assume that all classes observed at test time have been seen during
training. In this paper, we tackle the more realistic scenario where unexpected
objects of unknown classes can appear at test time. The main trends in this
area either leverage the notion of prediction uncertainty to flag the regions
with low confidence as unknown, or rely on autoencoders and highlight
poorly-decoded regions. Having observed that, in both cases, the detected
regions typically do not correspond to unexpected objects, in this paper, we
introduce a drastically different strategy: It relies on the intuition that the
network will produce spurious labels in regions depicting unexpected objects.
Therefore, resynthesizing the image from the resulting semantic map will yield
significant appearance differences with respect to the input image. In other
words, we translate the problem of detecting unknown classes to one of
identifying poorly-resynthesized image regions. We show that this outperforms
both uncertainty- and autoencoder-based methods
Detecting spatial patterns with the cumulant function. Part II: An application to El Nino
The spatial coherence of a measured variable (e.g. temperature or pressure)
is often studied to determine the regions where this variable varies the most
or to find teleconnections, i.e. correlations between specific regions. While
usual methods to find spatial patterns, such as Principal Components Analysis
(PCA), are constrained by linear symmetries, the dependence of variables such
as temperature or pressure at different locations is generally nonlinear. In
particular, large deviations from the sample mean are expected to be strongly
affected by such nonlinearities. Here we apply a newly developed nonlinear
technique (Maxima of Cumulant Function, MCF) for the detection of typical
spatial patterns that largely deviate from the mean. In order to test the
technique and to introduce the methodology, we focus on the El Nino/Southern
Oscillation and its spatial patterns. We find nonsymmetric temperature patterns
corresponding to El Nino and La Nina, and we compare the results of MCF with
other techniques, such as the symmetric solutions of PCA, and the nonsymmetric
solutions of Nonlinear PCA (NLPCA). We found that MCF solutions are more
reliable than the NLPCA fits, and can capture mixtures of principal components.
Finally, we apply Extreme Value Theory on the temporal variations extracted
from our methodology. We find that the tails of the distribution of extreme
temperatures during La Nina episodes is bounded, while the tail during El Ninos
is less likely to be bounded. This implies that the mean spatial patterns of
the two phases are asymmetric, as well as the behaviour of their extremes.Comment: 15 pages, 7 figure
Effect of water temperature on the courtship behavior of the Alpine newt Triturus alpestris
peer reviewedTemperature is expected to have an effect on the behavioral patterns of all organisms, especially ectotherms. However, although several studies focused on the effect of temperature on acoustic displays in both insects and anurans, almost nothing is known about how environmental temperature may affect ectotherm visual courtship displays and sexual performance. The purpose of this study was to determine the effect of environmental temperature on the sexual behavior of Alpine newts (Triturus alpestris). We subjected T. alpestris to two different temperatures in controlled laboratory conditions. Temperature had a major effect on both male and female behaviors: at low temperature, the frequencies of several displays, including tail-raising during sperm deposition, are lowered. This variation is caused indirectly by temperature because it is due to female responsiveness, which is temperature-dependent. However, the fanning movement of the male's tail during its main courtship display is independent of female behavior: at lower temperatures, the tail beats at a lower rate, but for a longer time. The similar reproductive success (i.e. sperm transfer) at the two temperature ranges indicates that breeding in cold water is not costly but instead allows males and females to mate early in the season. This is particularly adaptive because, in many habitats, the reproductive period is shortened by drying or freezing conditions, which may impair survival of branchiate offspring. This study also demonstrates the necessity of considering environmental parameters when modeling optimality and characteristics of ectotherm behaviors
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