2 research outputs found
Self-Paced Multi-Task Clustering
Multi-task clustering (MTC) has attracted a lot of research attentions in
machine learning due to its ability in utilizing the relationship among
different tasks. Despite the success of traditional MTC models, they are either
easy to stuck into local optima, or sensitive to outliers and noisy data. To
alleviate these problems, we propose a novel self-paced multi-task clustering
(SPMTC) paradigm. In detail, SPMTC progressively selects data examples to train
a series of MTC models with increasing complexity, thus highly decreases the
risk of trapping into poor local optima. Furthermore, to reduce the negative
influence of outliers and noisy data, we design a soft version of SPMTC to
further improve the clustering performance. The corresponding SPMTC framework
can be easily solved by an alternating optimization method. The proposed model
is guaranteed to converge and experiments on real data sets have demonstrated
its promising results compared with state-of-the-art multi-task clustering
methods
Self-Paced Deep Regression Forests for Facial Age Estimation
Facial age estimation is an important and challenging problem in computer
vision. Existing approaches usually employ deep neural networks (DNNs) to fit
the mapping from facial features to age, even though there exist some noisy and
confusing samples. We argue that it is more desirable to distinguish noisy and
confusing facial images from regular ones, and alleviate the interference
arising from them. To this end, we propose self-paced deep regression forests
(SP-DRFs) -- a gradual learning DNNs framework for age estimation. As the model
is learned gradually, from simplicity to complexity, it tends to emphasize more
on reliable samples and avoid bad local minima. Moreover, the proposed
capped-likelihood function helps to exclude noisy samples in training,
rendering our SP-DRFs significantly more robust. We demonstrate the efficacy of
SP-DRFs on Morph II and FG-NET datasets, where our model achieves
state-of-the-art performance.Comment: 7 pages, 5 figures, 2 table