4,133 research outputs found
Discussion of "EQUI-energy sampler" by Kou, Zhou and Wong
Discussion of ``EQUI-energy sampler'' by Kou, Zhou and Wong [math.ST/0507080]Comment: Published at http://dx.doi.org/10.1214/009053606000000506 in the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Interpretable Convolutional Neural Networks
This paper proposes a method to modify traditional convolutional neural
networks (CNNs) into interpretable CNNs, in order to clarify knowledge
representations in high conv-layers of CNNs. In an interpretable CNN, each
filter in a high conv-layer represents a certain object part. We do not need
any annotations of object parts or textures to supervise the learning process.
Instead, the interpretable CNN automatically assigns each filter in a high
conv-layer with an object part during the learning process. Our method can be
applied to different types of CNNs with different structures. The clear
knowledge representation in an interpretable CNN can help people understand the
logics inside a CNN, i.e., based on which patterns the CNN makes the decision.
Experiments showed that filters in an interpretable CNN were more semantically
meaningful than those in traditional CNNs.Comment: In this version, we release the website of the code. Compared to the
previous version, we have corrected all values of location instability in
Table 3--6 by dividing the values by sqrt(2), i.e., a=a/sqrt(2). Such
revisions do NOT decrease the significance of the superior performance of our
method, because we make the same correction to location-instability values of
all baseline
Learning Active Basis Models by EM-Type Algorithms
EM algorithm is a convenient tool for maximum likelihood model fitting when
the data are incomplete or when there are latent variables or hidden states. In
this review article we explain that EM algorithm is a natural computational
scheme for learning image templates of object categories where the learning is
not fully supervised. We represent an image template by an active basis model,
which is a linear composition of a selected set of localized, elongated and
oriented wavelet elements that are allowed to slightly perturb their locations
and orientations to account for the deformations of object shapes. The model
can be easily learned when the objects in the training images are of the same
pose, and appear at the same location and scale. This is often called
supervised learning. In the situation where the objects may appear at different
unknown locations, orientations and scales in the training images, we have to
incorporate the unknown locations, orientations and scales as latent variables
into the image generation process, and learn the template by EM-type
algorithms. The E-step imputes the unknown locations, orientations and scales
based on the currently learned template. This step can be considered
self-supervision, which involves using the current template to recognize the
objects in the training images. The M-step then relearns the template based on
the imputed locations, orientations and scales, and this is essentially the
same as supervised learning. So the EM learning process iterates between
recognition and supervised learning. We illustrate this scheme by several
experiments.Comment: Published in at http://dx.doi.org/10.1214/09-STS281 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Mining Object Parts from CNNs via Active Question-Answering
Given a convolutional neural network (CNN) that is pre-trained for object
classification, this paper proposes to use active question-answering to
semanticize neural patterns in conv-layers of the CNN and mine part concepts.
For each part concept, we mine neural patterns in the pre-trained CNN, which
are related to the target part, and use these patterns to construct an And-Or
graph (AOG) to represent a four-layer semantic hierarchy of the part. As an
interpretable model, the AOG associates different CNN units with different
explicit object parts. We use an active human-computer communication to
incrementally grow such an AOG on the pre-trained CNN as follows. We allow the
computer to actively identify objects, whose neural patterns cannot be
explained by the current AOG. Then, the computer asks human about the
unexplained objects, and uses the answers to automatically discover certain CNN
patterns corresponding to the missing knowledge. We incrementally grow the AOG
to encode new knowledge discovered during the active-learning process. In
experiments, our method exhibits high learning efficiency. Our method uses
about 1/6-1/3 of the part annotations for training, but achieves similar or
better part-localization performance than fast-RCNN methods.Comment: Published in CVPR 201
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