485 research outputs found
Extracting low-dimensional psychological representations from convolutional neural networks
Deep neural networks are increasingly being used in cognitive modeling as a
means of deriving representations for complex stimuli such as images. While the
predictive power of these networks is high, it is often not clear whether they
also offer useful explanations of the task at hand. Convolutional neural
network representations have been shown to be predictive of human similarity
judgments for images after appropriate adaptation. However, these
high-dimensional representations are difficult to interpret. Here we present a
method for reducing these representations to a low-dimensional space which is
still predictive of similarity judgments. We show that these low-dimensional
representations also provide insightful explanations of factors underlying
human similarity judgments.Comment: Accepted to CogSci 202
Learning a face space for experiments on human identity
Generative models of human identity and appearance have broad applicability
to behavioral science and technology, but the exquisite sensitivity of human
face perception means that their utility hinges on the alignment of the model's
representation to human psychological representations and the photorealism of
the generated images. Meeting these requirements is an exacting task, and
existing models of human identity and appearance are often unworkably abstract,
artificial, uncanny, or biased. Here, we use a variational autoencoder with an
autoregressive decoder to learn a face space from a uniquely diverse dataset of
portraits that control much of the variation irrelevant to human identity and
appearance. Our method generates photorealistic portraits of fictive identities
with a smooth, navigable latent space. We validate our model's alignment with
human sensitivities by introducing a psychophysical Turing test for images,
which humans mostly fail. Lastly, we demonstrate an initial application of our
model to the problem of fast search in mental space to obtain detailed "police
sketches" in a small number of trials.Comment: 10 figures. Accepted as a paper to the 40th Annual Meeting of the
Cognitive Science Society (CogSci 2018). *JWS and JCP contributed equally to
this submissio
Modeling Human Categorization of Natural Images Using Deep Feature Representations
Over the last few decades, psychologists have developed sophisticated formal
models of human categorization using simple artificial stimuli. In this paper,
we use modern machine learning methods to extend this work into the realm of
naturalistic stimuli, enabling human categorization to be studied over the
complex visual domain in which it evolved and developed. We show that
representations derived from a convolutional neural network can be used to
model behavior over a database of >300,000 human natural image classifications,
and find that a group of models based on these representations perform well,
near the reliability of human judgments. Interestingly, this group includes
both exemplar and prototype models, contrasting with the dominance of exemplar
models in previous work. We are able to improve the performance of the
remaining models by preprocessing neural network representations to more
closely capture human similarity judgments.Comment: 13 pages, 7 figures, 6 tables. Preliminary work presented at CogSci
201
Adapting Deep Network Features to Capture Psychological Representations
Deep neural networks have become increasingly successful atsolving classic perception problems such as object recognition,semantic segmentation, and scene understanding, often reach-ing or surpassing human-level accuracy. This success is duein part to the ability of DNNs to learn useful representationsof high-dimensional inputs, a problem that humans must alsosolve. We examine the relationship between the representa-tions learned by these networks and human psychological rep-resentations recovered from similarity judgments. We find thatdeep features learned in service of object classification accountfor a significant amount of the variance in human similarityjudgments for a set of animal images. However, these fea-tures do not capture some qualitative distinctions that are a keypart of human representations. To remedy this, we develop amethod for adapting deep features to align with human sim-ilarity judgments, resulting in image representations that canpotentially be used to extend the scope of psychological exper-iments
Learning Hierarchical Visual Representations in Deep Neural Networks Using Hierarchical Linguistic Labels
Modern convolutional neural networks (CNNs) are able to achieve human-level
object classification accuracy on specific tasks, and currently outperform
competing models in explaining complex human visual representations. However,
the categorization problem is posed differently for these networks than for
humans: the accuracy of these networks is evaluated by their ability to
identify single labels assigned to each image. These labels often cut
arbitrarily across natural psychological taxonomies (e.g., dogs are separated
into breeds, but never jointly categorized as "dogs"), and bias the resulting
representations. By contrast, it is common for children to hear both "dog" and
"Dalmatian" to describe the same stimulus, helping to group perceptually
disparate objects (e.g., breeds) into a common mental class. In this work, we
train CNN classifiers with multiple labels for each image that correspond to
different levels of abstraction, and use this framework to reproduce classic
patterns that appear in human generalization behavior.Comment: 6 pages, 4 figures, 1 table. Accepted as a paper to the 40th Annual
Meeting of the Cognitive Science Society (CogSci 2018
Capturing human category representations by sampling in deep feature spaces
Understanding how people represent categories is a core problem in cognitive
science. Decades of research have yielded a variety of formal theories of
categories, but validating them with naturalistic stimuli is difficult. The
challenge is that human category representations cannot be directly observed
and running informative experiments with naturalistic stimuli such as images
requires a workable representation of these stimuli. Deep neural networks have
recently been successful in solving a range of computer vision tasks and
provide a way to compactly represent image features. Here, we introduce a
method to estimate the structure of human categories that combines ideas from
cognitive science and machine learning, blending human-based algorithms with
state-of-the-art deep image generators. We provide qualitative and quantitative
results as a proof-of-concept for the method's feasibility. Samples drawn from
human distributions rival those from state-of-the-art generative models in
quality and outperform alternative methods for estimating the structure of
human categories.Comment: 6 pages, 5 figures, 1 table. Accepted as a paper to the 40th Annual
Meeting of the Cognitive Science Society (CogSci 2018
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Learning deep taxonomic priors for concept learning from few positive examples
Human concept learning is surprisingly robust, allowing forprecise generalizations given only a few positive examples.Bayesian formulations that account for this behavior requireelaborate, pre-specified priors, leaving much of the learningprocess unexplained. More recent models of concept learningbootstrap from deep representations, but the deep neural net-works are themselves trained using millions of positive and neg-ative examples. In machine learning, recent progress in meta-learning has provided large-scale learning algorithms that canlearn new concepts from a few examples, but these approachesstill assume access to implicit negative evidence. In this paper,we formulate a training paradigm that allows a meta-learningalgorithm to solve the problem of concept learning from fewpositive examples. The algorithm discovers a taxonomic prioruseful for learning novel concepts even from held-out supercat-egories and mimics human generalization behavior—the firstto do so without hand-specified domain knowledge or negativeexamples of a novel concept
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