Autoencoder, which learns latent representations
of samples in an unsupervised manner, has great potential in
computer vision and signal processing. However, the diversity of
samples makes learning a component autoencoder remaining a
challenging task. This letter proposes a novel Self-Paced AutoEncoder
(SPAE) for unsupervised feature extraction. The motivation
behind this letter is to take samples gradually from simple to
complex into consideration during training, which is similar to
the mechanism of knowledge acquisition for humans. Under the
unsupervised learning framework constructed on the autoencoder
infrastructure, our SPAE first learns a weak autoencoder via
samples with small losses and, then, elevates itself to a relatively
strong autoencoder through samples with large losses. Then, the
SPAE is generalized to a temporal domain, resulting to temporal
SPAE (TSPAE), where the temporal information is explored and
exploited to improve the performance. Typically, a TSPAE is capable
of compressing temporal sequences into temporal-independent
data. Experiments on the image classification and action
recognition demonstrate the effectiveness of SPAE and TSPAE.