1,367 research outputs found
Learning Generative Models with Visual Attention
Attention has long been proposed by psychologists as important for
effectively dealing with the enormous sensory stimulus available in the
neocortex. Inspired by the visual attention models in computational
neuroscience and the need of object-centric data for generative models, we
describe for generative learning framework using attentional mechanisms.
Attentional mechanisms can propagate signals from region of interest in a scene
to an aligned canonical representation, where generative modeling takes place.
By ignoring background clutter, generative models can concentrate their
resources on the object of interest. Our model is a proper graphical model
where the 2D Similarity transformation is a part of the top-down process. A
ConvNet is employed to provide good initializations during posterior inference
which is based on Hamiltonian Monte Carlo. Upon learning images of faces, our
model can robustly attend to face regions of novel test subjects. More
importantly, our model can learn generative models of new faces from a novel
dataset of large images where the face locations are not known.Comment: In the proceedings of Neural Information Processing Systems, 201
Learning Generative ConvNets via Multi-grid Modeling and Sampling
This paper proposes a multi-grid method for learning energy-based generative
ConvNet models of images. For each grid, we learn an energy-based probabilistic
model where the energy function is defined by a bottom-up convolutional neural
network (ConvNet or CNN). Learning such a model requires generating synthesized
examples from the model. Within each iteration of our learning algorithm, for
each observed training image, we generate synthesized images at multiple grids
by initializing the finite-step MCMC sampling from a minimal 1 x 1 version of
the training image. The synthesized image at each subsequent grid is obtained
by a finite-step MCMC initialized from the synthesized image generated at the
previous coarser grid. After obtaining the synthesized examples, the parameters
of the models at multiple grids are updated separately and simultaneously based
on the differences between synthesized and observed examples. We show that this
multi-grid method can learn realistic energy-based generative ConvNet models,
and it outperforms the original contrastive divergence (CD) and persistent CD.Comment: CVPR 201
Alternating Back-Propagation for Generator Network
This paper proposes an alternating back-propagation algorithm for learning
the generator network model. The model is a non-linear generalization of factor
analysis. In this model, the mapping from the continuous latent factors to the
observed signal is parametrized by a convolutional neural network. The
alternating back-propagation algorithm iterates the following two steps: (1)
Inferential back-propagation, which infers the latent factors by Langevin
dynamics or gradient descent. (2) Learning back-propagation, which updates the
parameters given the inferred latent factors by gradient descent. The gradient
computations in both steps are powered by back-propagation, and they share most
of their code in common. We show that the alternating back-propagation
algorithm can learn realistic generator models of natural images, video
sequences, and sounds. Moreover, it can also be used to learn from incomplete
or indirect training data
Deep Fishing: Gradient Features from Deep Nets
Convolutional Networks (ConvNets) have recently improved image recognition
performance thanks to end-to-end learning of deep feed-forward models from raw
pixels. Deep learning is a marked departure from the previous state of the art,
the Fisher Vector (FV), which relied on gradient-based encoding of local
hand-crafted features. In this paper, we discuss a novel connection between
these two approaches. First, we show that one can derive gradient
representations from ConvNets in a similar fashion to the FV. Second, we show
that this gradient representation actually corresponds to a structured matrix
that allows for efficient similarity computation. We experimentally study the
benefits of transferring this representation over the outputs of ConvNet
layers, and find consistent improvements on the Pascal VOC 2007 and 2012
datasets.Comment: To appear at BMVC 201
On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models
This study investigates the effects of Markov chain Monte Carlo (MCMC)
sampling in unsupervised Maximum Likelihood (ML) learning. Our attention is
restricted to the family of unnormalized probability densities for which the
negative log density (or energy function) is a ConvNet. We find that many of
the techniques used to stabilize training in previous studies are not
necessary. ML learning with a ConvNet potential requires only a few
hyper-parameters and no regularization. Using this minimal framework, we
identify a variety of ML learning outcomes that depend solely on the
implementation of MCMC sampling.
On one hand, we show that it is easy to train an energy-based model which can
sample realistic images with short-run Langevin. ML can be effective and stable
even when MCMC samples have much higher energy than true steady-state samples
throughout training. Based on this insight, we introduce an ML method with
purely noise-initialized MCMC, high-quality short-run synthesis, and the same
budget as ML with informative MCMC initialization such as CD or PCD. Unlike
previous models, our energy model can obtain realistic high-diversity samples
from a noise signal after training.
On the other hand, ConvNet potentials learned with non-convergent MCMC do not
have a valid steady-state and cannot be considered approximate unnormalized
densities of the training data because long-run MCMC samples differ greatly
from observed images. We show that it is much harder to train a ConvNet
potential to learn a steady-state over realistic images. To our knowledge,
long-run MCMC samples of all previous models lose the realism of short-run
samples. With correct tuning of Langevin noise, we train the first ConvNet
potentials for which long-run and steady-state MCMC samples are realistic
images.Comment: Code available at: https://github.com/point0bar1/ebm-anatom
Gradient-free activation maximization for identifying effective stimuli
A fundamental question for understanding brain function is what types of
stimuli drive neurons to fire. In visual neuroscience, this question has also
been posted as characterizing the receptive field of a neuron. The search for
effective stimuli has traditionally been based on a combination of insights
from previous studies, intuition, and luck. Recently, the same question has
emerged in the study of units in convolutional neural networks (ConvNets), and
together with this question a family of solutions were developed that are
generally referred to as "feature visualization by activation maximization."
We sought to bring in tools and techniques developed for studying ConvNets to
the study of biological neural networks. However, one key difference that
impedes direct translation of tools is that gradients can be obtained from
ConvNets using backpropagation, but such gradients are not available from the
brain. To circumvent this problem, we developed a method for gradient-free
activation maximization by combining a generative neural network with a genetic
algorithm. We termed this method XDream (EXtending DeepDream with real-time
evolution for activation maximization), and we have shown that this method can
reliably create strong stimuli for neurons in the macaque visual cortex (Ponce
et al., 2019). In this paper, we describe extensive experiments characterizing
the XDream method by using ConvNet units as in silico models of neurons. We
show that XDream is applicable across network layers, architectures, and
training sets; examine design choices in the algorithm; and provide practical
guides for choosing hyperparameters in the algorithm. XDream is an efficient
algorithm for uncovering neuronal tuning preferences in black-box networks
using a vast and diverse stimulus space.Comment: 16 pages, 8 figures, 3 table
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