103,862 research outputs found
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
Visualizing Deep Networks by Optimizing with Integrated Gradients
Understanding and interpreting the decisions made by deep learning models is
valuable in many domains. In computer vision, computing heatmaps from a deep
network is a popular approach for visualizing and understanding deep networks.
However, heatmaps that do not correlate with the network may mislead human,
hence the performance of heatmaps in providing a faithful explanation to the
underlying deep network is crucial. In this paper, we propose I-GOS, which
optimizes for a heatmap so that the classification scores on the masked image
would maximally decrease. The main novelty of the approach is to compute
descent directions based on the integrated gradients instead of the normal
gradient, which avoids local optima and speeds up convergence. Compared with
previous approaches, our method can flexibly compute heatmaps at any resolution
for different user needs. Extensive experiments on several benchmark datasets
show that the heatmaps produced by our approach are more correlated with the
decision of the underlying deep network, in comparison with other
state-of-the-art approaches
- …