1,292 research outputs found
Representation Learning: A Review and New Perspectives
The success of machine learning algorithms generally depends on data
representation, and we hypothesize that this is because different
representations can entangle and hide more or less the different explanatory
factors of variation behind the data. Although specific domain knowledge can be
used to help design representations, learning with generic priors can also be
used, and the quest for AI is motivating the design of more powerful
representation-learning algorithms implementing such priors. This paper reviews
recent work in the area of unsupervised feature learning and deep learning,
covering advances in probabilistic models, auto-encoders, manifold learning,
and deep networks. This motivates longer-term unanswered questions about the
appropriate objectives for learning good representations, for computing
representations (i.e., inference), and the geometrical connections between
representation learning, density estimation and manifold learning
Deep Learning of Representations: Looking Forward
Deep learning research aims at discovering learning algorithms that discover
multiple levels of distributed representations, with higher levels representing
more abstract concepts. Although the study of deep learning has already led to
impressive theoretical results, learning algorithms and breakthrough
experiments, several challenges lie ahead. This paper proposes to examine some
of these challenges, centering on the questions of scaling deep learning
algorithms to much larger models and datasets, reducing optimization
difficulties due to ill-conditioning or local minima, designing more efficient
and powerful inference and sampling procedures, and learning to disentangle the
factors of variation underlying the observed data. It also proposes a few
forward-looking research directions aimed at overcoming these challenges
Mimicking non-ideal instrument behavior for hologram processing using neural style translation
Holographic cloud probes provide unprecedented information on cloud particle
density, size and position. Each laser shot captures particles within a large
volume, where images can be computationally refocused to determine particle
size and shape. However, processing these holograms, either with standard
methods or with machine learning (ML) models, requires considerable
computational resources, time and occasional human intervention. ML models are
trained on simulated holograms obtained from the physical model of the probe
since real holograms have no absolute truth labels. Using another processing
method to produce labels would be subject to errors that the ML model would
subsequently inherit. Models perform well on real holograms only when image
corruption is performed on the simulated images during training, thereby
mimicking non-ideal conditions in the actual probe (Schreck et. al, 2022).
Optimizing image corruption requires a cumbersome manual labeling effort.
Here we demonstrate the application of the neural style translation approach
(Gatys et. al, 2016) to the simulated holograms. With a pre-trained
convolutional neural network (VGG-19), the simulated holograms are ``stylized''
to resemble the real ones obtained from the probe, while at the same time
preserving the simulated image ``content'' (e.g. the particle locations and
sizes). Two image similarity metrics concur that the stylized images are more
like real holograms than the synthetic ones. With an ML model trained to
predict particle locations and shapes on the stylized data sets, we observed
comparable performance on both simulated and real holograms, obviating the need
to perform manual labeling. The described approach is not specific to hologram
images and could be applied in other domains for capturing noise and
imperfections in observational instruments to make simulated data more like
real world observations.Comment: 23 pages, 9 figure
A survey of face recognition techniques under occlusion
The limited capacity to recognize faces under occlusions is a long-standing
problem that presents a unique challenge for face recognition systems and even
for humans. The problem regarding occlusion is less covered by research when
compared to other challenges such as pose variation, different expressions,
etc. Nevertheless, occluded face recognition is imperative to exploit the full
potential of face recognition for real-world applications. In this paper, we
restrict the scope to occluded face recognition. First, we explore what the
occlusion problem is and what inherent difficulties can arise. As a part of
this review, we introduce face detection under occlusion, a preliminary step in
face recognition. Second, we present how existing face recognition methods cope
with the occlusion problem and classify them into three categories, which are
1) occlusion robust feature extraction approaches, 2) occlusion aware face
recognition approaches, and 3) occlusion recovery based face recognition
approaches. Furthermore, we analyze the motivations, innovations, pros and
cons, and the performance of representative approaches for comparison. Finally,
future challenges and method trends of occluded face recognition are thoroughly
discussed
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