1,461 research outputs found
Lifelong Learning of Spatiotemporal Representations with Dual-Memory Recurrent Self-Organization
Artificial autonomous agents and robots interacting in complex environments
are required to continually acquire and fine-tune knowledge over sustained
periods of time. The ability to learn from continuous streams of information is
referred to as lifelong learning and represents a long-standing challenge for
neural network models due to catastrophic forgetting. Computational models of
lifelong learning typically alleviate catastrophic forgetting in experimental
scenarios with given datasets of static images and limited complexity, thereby
differing significantly from the conditions artificial agents are exposed to.
In more natural settings, sequential information may become progressively
available over time and access to previous experience may be restricted. In
this paper, we propose a dual-memory self-organizing architecture for lifelong
learning scenarios. The architecture comprises two growing recurrent networks
with the complementary tasks of learning object instances (episodic memory) and
categories (semantic memory). Both growing networks can expand in response to
novel sensory experience: the episodic memory learns fine-grained
spatiotemporal representations of object instances in an unsupervised fashion
while the semantic memory uses task-relevant signals to regulate structural
plasticity levels and develop more compact representations from episodic
experience. For the consolidation of knowledge in the absence of external
sensory input, the episodic memory periodically replays trajectories of neural
reactivations. We evaluate the proposed model on the CORe50 benchmark dataset
for continuous object recognition, showing that we significantly outperform
current methods of lifelong learning in three different incremental learning
scenario
iTAML: An Incremental Task-Agnostic Meta-learning Approach
Humans can continuously learn new knowledge as their experience grows. In
contrast, previous learning in deep neural networks can quickly fade out when
they are trained on a new task. In this paper, we hypothesize this problem can
be avoided by learning a set of generalized parameters, that are neither
specific to old nor new tasks. In this pursuit, we introduce a novel
meta-learning approach that seeks to maintain an equilibrium between all the
encountered tasks. This is ensured by a new meta-update rule which avoids
catastrophic forgetting. In comparison to previous meta-learning techniques,
our approach is task-agnostic. When presented with a continuum of data, our
model automatically identifies the task and quickly adapts to it with just a
single update. We perform extensive experiments on five datasets in a
class-incremental setting, leading to significant improvements over the state
of the art methods (e.g., a 21.3% boost on CIFAR100 with 10 incremental tasks).
Specifically, on large-scale datasets that generally prove difficult cases for
incremental learning, our approach delivers absolute gains as high as 19.1% and
7.4% on ImageNet and MS-Celeb datasets, respectively.Comment: Accepted to CVPR 202
Bio-Inspired Techniques in a Fully Digital Approach for Lifelong Learning
open3noLifelong learning has deeply underpinned the resilience of biological organisms respect to a constantly changing environment. This flexibility has allowed the evolution of parallel-distributed systems able to merge past information with new stimulus for accurate and efficient brain-computation. Nowadays, there is a strong attempt to reproduce such intelligent systems in standard artificial neural networks (ANNs). However, despite some great results in specific tasks, ANNs still appear too rigid and static in real life respect to the biological systems. Thus, it is necessary to define a new neural paradigm capable of merging the lifelong resilience of biological organisms with the great accuracy of ANNs. Here, we present a digital implementation of a novel mixed supervised-unsupervised neural network capable of performing lifelong learning. The network uses a set of convolutional filters to extract features from the input images of the MNIST and the Fashion-MNIST training datasets. This information defines an original combination of responses of both trained classes and non-trained classes by transfer learning. The responses are then used in the subsequent unsupervised learning based on spike-timing dependent plasticity (STDP). This procedure allows the clustering of non-trained information thanks to bio-inspired algorithms such as neuronal redundancy and spike-frequency adaptation. We demonstrate the implementation of the neural network in a fully digital environment, such as the Xilinx Zynq-7000 System on Chip (SoC). We illustrate a user-friendly interface to test the network by choosing the number and the type of the non-trained classes, or drawing a custom pattern on a tablet. Finally, we propose a comparison of this work with networks based on memristive synaptic devices capable of continual learning, highlighting the main differences and capabilities respect to a fully digital approach.openBianchi S.; MUĂ‘OZ MARTĂŤN IRENE; Ielmini D.Bianchi, S.; MUĂ‘OZ MARTĂŤN, Irene; Ielmini, D
An Introduction to Lifelong Supervised Learning
This primer is an attempt to provide a detailed summary of the different
facets of lifelong learning. We start with Chapter 2 which provides a
high-level overview of lifelong learning systems. In this chapter, we discuss
prominent scenarios in lifelong learning (Section 2.4), provide 8 Introduction
a high-level organization of different lifelong learning approaches (Section
2.5), enumerate the desiderata for an ideal lifelong learning system (Section
2.6), discuss how lifelong learning is related to other learning paradigms
(Section 2.7), describe common metrics used to evaluate lifelong learning
systems (Section 2.8). This chapter is more useful for readers who are new to
lifelong learning and want to get introduced to the field without focusing on
specific approaches or benchmarks. The remaining chapters focus on specific
aspects (either learning algorithms or benchmarks) and are more useful for
readers who are looking for specific approaches or benchmarks. Chapter 3
focuses on regularization-based approaches that do not assume access to any
data from previous tasks. Chapter 4 discusses memory-based approaches that
typically use a replay buffer or an episodic memory to save subset of data
across different tasks. Chapter 5 focuses on different architecture families
(and their instantiations) that have been proposed for training lifelong
learning systems. Following these different classes of learning algorithms, we
discuss the commonly used evaluation benchmarks and metrics for lifelong
learning (Chapter 6) and wrap up with a discussion of future challenges and
important research directions in Chapter 7.Comment: Lifelong Learning Prime
A Unified Approach to Domain Incremental Learning with Memory: Theory and Algorithm
Domain incremental learning aims to adapt to a sequence of domains with
access to only a small subset of data (i.e., memory) from previous domains.
Various methods have been proposed for this problem, but it is still unclear
how they are related and when practitioners should choose one method over
another. In response, we propose a unified framework, dubbed Unified Domain
Incremental Learning (UDIL), for domain incremental learning with memory. Our
UDIL **unifies** various existing methods, and our theoretical analysis shows
that UDIL always achieves a tighter generalization error bound compared to
these methods. The key insight is that different existing methods correspond to
our bound with different **fixed** coefficients; based on insights from this
unification, our UDIL allows **adaptive** coefficients during training, thereby
always achieving the tightest bound. Empirical results show that our UDIL
outperforms the state-of-the-art domain incremental learning methods on both
synthetic and real-world datasets. Code will be available at
https://github.com/Wang-ML-Lab/unified-continual-learning.Comment: Accepted at NeurIPS 202
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