6,528 research outputs found
Scalable Recollections for Continual Lifelong Learning
Given the recent success of Deep Learning applied to a variety of single
tasks, it is natural to consider more human-realistic settings. Perhaps the
most difficult of these settings is that of continual lifelong learning, where
the model must learn online over a continuous stream of non-stationary data. A
successful continual lifelong learning system must have three key capabilities:
it must learn and adapt over time, it must not forget what it has learned, and
it must be efficient in both training time and memory. Recent techniques have
focused their efforts primarily on the first two capabilities while questions
of efficiency remain largely unexplored. In this paper, we consider the problem
of efficient and effective storage of experiences over very large time-frames.
In particular we consider the case where typical experiences are O(n) bits and
memories are limited to O(k) bits for k << n. We present a novel scalable
architecture and training algorithm in this challenging domain and provide an
extensive evaluation of its performance. Our results show that we can achieve
considerable gains on top of state-of-the-art methods such as GEM.Comment: AAAI 201
CORe50: a New Dataset and Benchmark for Continuous Object Recognition
Continuous/Lifelong learning of high-dimensional data streams is a
challenging research problem. In fact, fully retraining models each time new
data become available is infeasible, due to computational and storage issues,
while na\"ive incremental strategies have been shown to suffer from
catastrophic forgetting. In the context of real-world object recognition
applications (e.g., robotic vision), where continuous learning is crucial, very
few datasets and benchmarks are available to evaluate and compare emerging
techniques. In this work we propose a new dataset and benchmark CORe50,
specifically designed for continuous object recognition, and introduce baseline
approaches for different continuous learning scenarios
iCaRL: Incremental Classifier and Representation Learning
A major open problem on the road to artificial intelligence is the
development of incrementally learning systems that learn about more and more
concepts over time from a stream of data. In this work, we introduce a new
training strategy, iCaRL, that allows learning in such a class-incremental way:
only the training data for a small number of classes has to be present at the
same time and new classes can be added progressively. iCaRL learns strong
classifiers and a data representation simultaneously. This distinguishes it
from earlier works that were fundamentally limited to fixed data
representations and therefore incompatible with deep learning architectures. We
show by experiments on CIFAR-100 and ImageNet ILSVRC 2012 data that iCaRL can
learn many classes incrementally over a long period of time where other
strategies quickly fail.Comment: Accepted paper at CVPR 201
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