98,515 research outputs found
Reduction from Complementary-Label Learning to Probability Estimates
Complementary-Label Learning (CLL) is a weakly-supervised learning problem
that aims to learn a multi-class classifier from only complementary labels,
which indicate a class to which an instance does not belong. Existing
approaches mainly adopt the paradigm of reduction to ordinary classification,
which applies specific transformations and surrogate losses to connect CLL back
to ordinary classification. Those approaches, however, face several
limitations, such as the tendency to overfit or be hooked on deep models. In
this paper, we sidestep those limitations with a novel perspective--reduction
to probability estimates of complementary classes. We prove that accurate
probability estimates of complementary labels lead to good classifiers through
a simple decoding step. The proof establishes a reduction framework from CLL to
probability estimates. The framework offers explanations of several key CLL
approaches as its special cases and allows us to design an improved algorithm
that is more robust in noisy environments. The framework also suggests a
validation procedure based on the quality of probability estimates, leading to
an alternative way to validate models with only complementary labels. The
flexible framework opens a wide range of unexplored opportunities in using deep
and non-deep models for probability estimates to solve the CLL problem.
Empirical experiments further verified the framework's efficacy and robustness
in various settings
Multi-View Face Recognition From Single RGBD Models of the Faces
This work takes important steps towards solving the following problem of current interest: Assuming that each individual in a population can be modeled by a single frontal RGBD face image, is it possible to carry out face recognition for such a population using multiple 2D images captured from arbitrary viewpoints? Although the general problem as stated above is extremely challenging, it encompasses subproblems that can be addressed today. The subproblems addressed in this work relate to: (1) Generating a large set of viewpoint dependent face images from a single RGBD frontal image for each individual; (2) using hierarchical approaches based on view-partitioned subspaces to represent the training data; and (3) based on these hierarchical approaches, using a weighted voting algorithm to integrate the evidence collected from multiple images of the same face as recorded from different viewpoints. We evaluate our methods on three datasets: a dataset of 10 people that we created and two publicly available datasets which include a total of 48 people. In addition to providing important insights into the nature of this problem, our results show that we are able to successfully recognize faces with accuracies of 95% or higher, outperforming existing state-of-the-art face recognition approaches based on deep convolutional neural networks
MOON: A Mixed Objective Optimization Network for the Recognition of Facial Attributes
Attribute recognition, particularly facial, extracts many labels for each
image. While some multi-task vision problems can be decomposed into separate
tasks and stages, e.g., training independent models for each task, for a
growing set of problems joint optimization across all tasks has been shown to
improve performance. We show that for deep convolutional neural network (DCNN)
facial attribute extraction, multi-task optimization is better. Unfortunately,
it can be difficult to apply joint optimization to DCNNs when training data is
imbalanced, and re-balancing multi-label data directly is structurally
infeasible, since adding/removing data to balance one label will change the
sampling of the other labels. This paper addresses the multi-label imbalance
problem by introducing a novel mixed objective optimization network (MOON) with
a loss function that mixes multiple task objectives with domain adaptive
re-weighting of propagated loss. Experiments demonstrate that not only does
MOON advance the state of the art in facial attribute recognition, but it also
outperforms independently trained DCNNs using the same data. When using facial
attributes for the LFW face recognition task, we show that our balanced (domain
adapted) network outperforms the unbalanced trained network.Comment: Post-print of manuscript accepted to the European Conference on
Computer Vision (ECCV) 2016
http://link.springer.com/chapter/10.1007%2F978-3-319-46454-1_
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