35,152 research outputs found
Mode Variational LSTM Robust to Unseen Modes of Variation: Application to Facial Expression Recognition
Spatio-temporal feature encoding is essential for encoding the dynamics in
video sequences. Recurrent neural networks, particularly long short-term memory
(LSTM) units, have been popular as an efficient tool for encoding
spatio-temporal features in sequences. In this work, we investigate the effect
of mode variations on the encoded spatio-temporal features using LSTMs. We show
that the LSTM retains information related to the mode variation in the
sequence, which is irrelevant to the task at hand (e.g. classification facial
expressions). Actually, the LSTM forget mechanism is not robust enough to mode
variations and preserves information that could negatively affect the encoded
spatio-temporal features. We propose the mode variational LSTM to encode
spatio-temporal features robust to unseen modes of variation. The mode
variational LSTM modifies the original LSTM structure by adding an additional
cell state that focuses on encoding the mode variation in the input sequence.
To efficiently regulate what features should be stored in the additional cell
state, additional gating functionality is also introduced. The effectiveness of
the proposed mode variational LSTM is verified using the facial expression
recognition task. Comparative experiments on publicly available datasets
verified that the proposed mode variational LSTM outperforms existing methods.
Moreover, a new dynamic facial expression dataset with different modes of
variation, including various modes like pose and illumination variations, was
collected to comprehensively evaluate the proposed mode variational LSTM.
Experimental results verified that the proposed mode variational LSTM encodes
spatio-temporal features robust to unseen modes of variation.Comment: Accepted in AAAI-1
Supernova-driven outflows and chemical evolution of dwarf spheroidal galaxies
We present a general phenomenological model for the metallicity distribution
(MD) in terms of [Fe/H] for dwarf spheroidal galaxies (dSphs). These galaxies
appear to have stopped accreting gas from the intergalactic medium and are
fossilized systems with their stars undergoing slow internal evolution. For a
wide variety of infall histories of unprocessed baryonic matter to feed star
formation, most of the observed MDs can be well described by our model. The key
requirement is that the fraction of the gas mass lost by supernova-driven
outflows is close to unity. This model also predicts a relationship between the
total stellar mass and the mean metallicity for dSphs in accord with properties
of their dark matter halos. The model further predicts as a natural consequence
that the abundance ratios [E/Fe] for elements such as O, Mg, and Si decrease
for stellar populations at the higher end of the [Fe/H] range in a dSph. We
show that for infall rates far below the net rate of gas loss to star formation
and outflows, the MD in our model is very sharply peaked at one [Fe/H] value,
similar to what is observed in most globular clusters. This suggests that
globular clusters may be end members of the same family as dSphs.Comment: 8 pages, 3 figures, to be published in the Proceedings of the
National Academy of Science
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