28,704 research outputs found
Computational Baby Learning
Intuitive observations show that a baby may inherently possess the capability
of recognizing a new visual concept (e.g., chair, dog) by learning from only
very few positive instances taught by parent(s) or others, and this recognition
capability can be gradually further improved by exploring and/or interacting
with the real instances in the physical world. Inspired by these observations,
we propose a computational model for slightly-supervised object detection,
based on prior knowledge modelling, exemplar learning and learning with video
contexts. The prior knowledge is modeled with a pre-trained Convolutional
Neural Network (CNN). When very few instances of a new concept are given, an
initial concept detector is built by exemplar learning over the deep features
from the pre-trained CNN. Simulating the baby's interaction with physical
world, the well-designed tracking solution is then used to discover more
diverse instances from the massive online unlabeled videos. Once a positive
instance is detected/identified with high score in each video, more variable
instances possibly from different view-angles and/or different distances are
tracked and accumulated. Then the concept detector can be fine-tuned based on
these new instances. This process can be repeated again and again till we
obtain a very mature concept detector. Extensive experiments on Pascal
VOC-07/10/12 object detection datasets well demonstrate the effectiveness of
our framework. It can beat the state-of-the-art full-training based
performances by learning from very few samples for each object category, along
with about 20,000 unlabeled videos.Comment: 9 page
Intrinsic Motivation Systems for Autonomous Mental Development
Exploratory activities seem to be intrinsically rewarding
for children and crucial for their cognitive development.
Can a machine be endowed with such an intrinsic motivation
system? This is the question we study in this paper, presenting a number of computational systems that try to capture this drive towards novel or curious situations. After discussing related research coming from developmental psychology, neuroscience, developmental robotics, and active learning, this paper presents the mechanism of Intelligent Adaptive Curiosity, an intrinsic motivation system which pushes a robot towards situations in which it maximizes its learning progress. This drive makes the robot focus on situations which are neither too predictable nor too unpredictable, thus permitting autonomous mental development.The complexity of the robot’s activities autonomously increases and complex developmental sequences self-organize without being constructed in a supervised manner. Two experiments are presented illustrating the stage-like organization emerging with this mechanism. In one of them, a physical robot is placed on a baby play mat with objects that it can learn to manipulate. Experimental results show that the robot first spends time in situations
which are easy to learn, then shifts its attention progressively to situations of increasing difficulty, avoiding situations in which nothing can be learned. Finally, these various results are discussed in relation to more complex forms of behavioral organization and data coming from developmental psychology.
Key words: Active learning, autonomy, behavior, complexity,
curiosity, development, developmental trajectory, epigenetic
robotics, intrinsic motivation, learning, reinforcement learning,
values
Automatic Environmental Sound Recognition: Performance versus Computational Cost
In the context of the Internet of Things (IoT), sound sensing applications
are required to run on embedded platforms where notions of product pricing and
form factor impose hard constraints on the available computing power. Whereas
Automatic Environmental Sound Recognition (AESR) algorithms are most often
developed with limited consideration for computational cost, this article seeks
which AESR algorithm can make the most of a limited amount of computing power
by comparing the sound classification performance em as a function of its
computational cost. Results suggest that Deep Neural Networks yield the best
ratio of sound classification accuracy across a range of computational costs,
while Gaussian Mixture Models offer a reasonable accuracy at a consistently
small cost, and Support Vector Machines stand between both in terms of
compromise between accuracy and computational cost
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