30,975 research outputs found
Unmasking Clever Hans Predictors and Assessing What Machines Really Learn
Current learning machines have successfully solved hard application problems,
reaching high accuracy and displaying seemingly "intelligent" behavior. Here we
apply recent techniques for explaining decisions of state-of-the-art learning
machines and analyze various tasks from computer vision and arcade games. This
showcases a spectrum of problem-solving behaviors ranging from naive and
short-sighted, to well-informed and strategic. We observe that standard
performance evaluation metrics can be oblivious to distinguishing these diverse
problem solving behaviors. Furthermore, we propose our semi-automated Spectral
Relevance Analysis that provides a practically effective way of characterizing
and validating the behavior of nonlinear learning machines. This helps to
assess whether a learned model indeed delivers reliably for the problem that it
was conceived for. Furthermore, our work intends to add a voice of caution to
the ongoing excitement about machine intelligence and pledges to evaluate and
judge some of these recent successes in a more nuanced manner.Comment: Accepted for publication in Nature Communication
Keeping track of worm trackers
C. elegans is used extensively as a model system in the neurosciences due to its well defined nervous system. However, the seeming simplicity of this nervous system in anatomical structure and neuronal connectivity, at least compared to higher animals, underlies a rich diversity of behaviors. The usefulness of the worm in genome-wide mutagenesis or RNAi screens, where thousands of strains are assessed for phenotype, emphasizes the need for computational methods for automated parameterization of generated behaviors. In addition, behaviors can be modulated upon external cues like temperature, O2 and CO2 concentrations, mechanosensory and chemosensory inputs. Different machine vision tools have been developed to aid researchers in their efforts to inventory and characterize defined behavioral “outputs”. Here we aim at providing an overview of different worm-tracking packages or video analysis tools designed to quantify different aspects of locomotion such as the occurrence of directional changes (turns, omega bends), curvature of the sinusoidal shape (amplitude, body bend angles) and velocity (speed, backward or forward movement)
Beyond saliency: understanding convolutional neural networks from saliency prediction on layer-wise relevance propagation
Despite the tremendous achievements of deep convolutional neural networks
(CNNs) in many computer vision tasks, understanding how they actually work
remains a significant challenge. In this paper, we propose a novel two-step
understanding method, namely Salient Relevance (SR) map, which aims to shed
light on how deep CNNs recognize images and learn features from areas, referred
to as attention areas, therein. Our proposed method starts out with a
layer-wise relevance propagation (LRP) step which estimates a pixel-wise
relevance map over the input image. Following, we construct a context-aware
saliency map, SR map, from the LRP-generated map which predicts areas close to
the foci of attention instead of isolated pixels that LRP reveals. In human
visual system, information of regions is more important than of pixels in
recognition. Consequently, our proposed approach closely simulates human
recognition. Experimental results using the ILSVRC2012 validation dataset in
conjunction with two well-established deep CNN models, AlexNet and VGG-16,
clearly demonstrate that our proposed approach concisely identifies not only
key pixels but also attention areas that contribute to the underlying neural
network's comprehension of the given images. As such, our proposed SR map
constitutes a convenient visual interface which unveils the visual attention of
the network and reveals which type of objects the model has learned to
recognize after training. The source code is available at
https://github.com/Hey1Li/Salient-Relevance-Propagation.Comment: 35 pages, 15 figure
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