3,000 research outputs found
Online Continual Learning in Keyword Spotting for Low-Resource Devices via Pooling High-Order Temporal Statistics
Keyword Spotting (KWS) models on embedded devices should adapt fast to new
user-defined words without forgetting previous ones. Embedded devices have
limited storage and computational resources, thus, they cannot save samples or
update large models. We consider the setup of embedded online continual
learning (EOCL), where KWS models with frozen backbone are trained to
incrementally recognize new words from a non-repeated stream of samples, seen
one at a time. To this end, we propose Temporal Aware Pooling (TAP) which
constructs an enriched feature space computing high-order moments of speech
features extracted by a pre-trained backbone. Our method, TAP-SLDA, updates a
Gaussian model for each class on the enriched feature space to effectively use
audio representations. In experimental analyses, TAP-SLDA outperforms
competitors on several setups, backbones, and baselines, bringing a relative
average gain of 11.3% on the GSC dataset.Comment: INTERSPEECH 202
Machine learning and its applications in reliability analysis systems
In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA
A Comprehensive Performance Evaluation of Deformable Face Tracking "In-the-Wild"
Recently, technologies such as face detection, facial landmark localisation
and face recognition and verification have matured enough to provide effective
and efficient solutions for imagery captured under arbitrary conditions
(referred to as "in-the-wild"). This is partially attributed to the fact that
comprehensive "in-the-wild" benchmarks have been developed for face detection,
landmark localisation and recognition/verification. A very important technology
that has not been thoroughly evaluated yet is deformable face tracking
"in-the-wild". Until now, the performance has mainly been assessed
qualitatively by visually assessing the result of a deformable face tracking
technology on short videos. In this paper, we perform the first, to the best of
our knowledge, thorough evaluation of state-of-the-art deformable face tracking
pipelines using the recently introduced 300VW benchmark. We evaluate many
different architectures focusing mainly on the task of on-line deformable face
tracking. In particular, we compare the following general strategies: (a)
generic face detection plus generic facial landmark localisation, (b) generic
model free tracking plus generic facial landmark localisation, as well as (c)
hybrid approaches using state-of-the-art face detection, model free tracking
and facial landmark localisation technologies. Our evaluation reveals future
avenues for further research on the topic.Comment: E. Antonakos and P. Snape contributed equally and have joint second
authorshi
Towards Efficient Lifelong Machine Learning in Deep Neural Networks
Humans continually learn and adapt to new knowledge and environments throughout their lifetimes. Rarely does learning new information cause humans to catastrophically forget previous knowledge. While deep neural networks (DNNs) now rival human performance on several supervised machine perception tasks, when updated on changing data distributions, they catastrophically forget previous knowledge. Enabling DNNs to learn new information over time opens the door for new applications such as self-driving cars that adapt to seasonal changes or smartphones that adapt to changing user preferences. In this dissertation, we propose new methods and experimental paradigms for efficiently training continual DNNs without forgetting. We then apply these methods to several visual and multi-modal perception tasks including image classification, visual question answering, analogical reasoning, and attribute and relationship prediction in visual scenes
Neuromorphic Incremental on-chip Learning with Hebbian Weight Consolidation
As next-generation implantable brain-machine interfaces become pervasive on
edge device, incrementally learning new tasks in bio-plasticity ways is
urgently demanded for Neuromorphic chips. Due to the inherent characteristics
of its structure, spiking neural networks are naturally well-suited for
BMI-chips. Here we propose Hebbian Weight Consolidation, as well as an on-chip
learning framework. HWC selectively masks synapse modifications for previous
tasks, retaining them to store new knowledge from subsequent tasks while
preserving the old knowledge. Leveraging the bio-plasticity of dendritic
spines, the intrinsic self-organizing nature of Hebbian Weight Consolidation
aligns naturally with the incremental learning paradigm, facilitating robust
learning outcomes. By reading out spikes layer by layer and performing
back-propagation on the external micro-controller unit, MLoC can efficiently
accomplish on-chip learning. Experiments show that our HWC algorithm up to
23.19% outperforms lower bound that without incremental learning algorithm,
particularly in more challenging monkey behavior decoding scenarios. Taking
into account on-chip computing on Synsense Speck 2e chip, our proposed
algorithm exhibits an improvement of 11.06%. This study demonstrates the
feasibility of employing incremental learning for high-performance neural
signal decoding in next-generation brain-machine interfaces.Comment: 12 pages, 6 figure
A Conceptual Architecture for Enabling Future Self-Adaptive Service Systems
Dynamic integration methods for unknown data sources and services at system design time are currently primarily driven by technological standards. Hence, little emphasis is being placed on integration methods. However, the combination of heterogeneous data sources and services offered by devices across domains is hard to standardize. In this paper, we will shed light on the interplay of self-adaptive system architectures as well as bottom-up, incremental integration methods relying on formal knowledge bases. An incremental integration method has direct influences on both the system architecture itself and the way these systems are engineered and operated during design and runtime. Our findings are evaluated in the context of a case study that uses an adapted bus architecture including two tool prototypes. In addition, we illustrate conceptually how control loops such as MAPE-K can be enriched with machine-readable integration knowledge
- …