249 research outputs found
A Developmental Neuro-Robotics Approach for Boosting the Recognition of Handwritten Digits
Developmental psychology and neuroimaging
research identified a close link between numbers and fingers,
which can boost the initial number knowledge in children. Recent
evidence shows that a simulation of the children's embodied
strategies can improve the machine intelligence too. This article
explores the application of embodied strategies to convolutional
neural network models in the context of developmental neurorobotics, where the training information is likely to be gradually
acquired while operating rather than being abundant and fully
available as the classical machine learning scenarios. The
experimental analyses show that the proprioceptive information
from the robot fingers can improve network accuracy in the
recognition of handwritten Arabic digits when training examples
and epochs are few. This result is comparable to brain imaging
and longitudinal studies with young children. In conclusion, these
findings also support the relevance of the embodiment in the case
of artificial agents’ training and show a possible way for the
humanization of the learning process, where the robotic body can
express the internal processes of artificial intelligence making it
more understandable for humans
Loss of Plasticity in Deep Continual Learning
Modern deep-learning systems are specialized to problem settings in which
training occurs once and then never again, as opposed to continual-learning
settings in which training occurs continually. If deep-learning systems are
applied in a continual learning setting, then it is well known that they may
fail to remember earlier examples. More fundamental, but less well known, is
that they may also lose their ability to learn on new examples, a phenomenon
called loss of plasticity. We provide direct demonstrations of loss of
plasticity using the MNIST and ImageNet datasets repurposed for continual
learning as sequences of tasks. In ImageNet, binary classification performance
dropped from 89\% accuracy on an early task down to 77\%, about the level of a
linear network, on the 2000th task. Loss of plasticity occurred with a wide
range of deep network architectures, optimizers, activation functions, batch
normalization, dropout, but was substantially eased by -regularization,
particularly when combined with weight perturbation. Further, we introduce a
new algorithm -- continual backpropagation -- which slightly modifies
conventional backpropagation to reinitialize a small fraction of less-used
units after each example and appears to maintain plasticity indefinitely
Smart Augmentation - Learning an Optimal Data Augmentation Strategy
A recurring problem faced when training neural networks is that there is
typically not enough data to maximize the generalization capability of deep
neural networks(DNN). There are many techniques to address this, including data
augmentation, dropout, and transfer learning. In this paper, we introduce an
additional method which we call Smart Augmentation and we show how to use it to
increase the accuracy and reduce overfitting on a target network. Smart
Augmentation works by creating a network that learns how to generate augmented
data during the training process of a target network in a way that reduces that
networks loss. This allows us to learn augmentations that minimize the error of
that network.
Smart Augmentation has shown the potential to increase accuracy by
demonstrably significant measures on all datasets tested. In addition, it has
shown potential to achieve similar or improved performance levels with
significantly smaller network sizes in a number of tested cases
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