3 research outputs found
Continual Learning of Object Instances
We propose continual instance learning - a method that applies the concept of
continual learning to the task of distinguishing instances of the same object
category. We specifically focus on the car object, and incrementally learn to
distinguish car instances from each other with metric learning. We begin our
paper by evaluating current techniques. Establishing that catastrophic
forgetting is evident in existing methods, we then propose two remedies.
Firstly, we regularise metric learning via Normalised Cross-Entropy. Secondly,
we augment existing models with synthetic data transfer. Our extensive
experiments on three large-scale datasets, using two different architectures
for five different continual learning methods, reveal that Normalised
cross-entropy and synthetic transfer leads to less forgetting in existing
techniques.Comment: Accepted to CVPR 2020: Workshop on Continual Learning in Computer
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Deep Metric Learning for the Hemodynamics Inference with Electrocardiogram Signals
Heart failure is a debilitating condition that affects millions of people
worldwide and has a significant impact on their quality of life and mortality
rates. An objective assessment of cardiac pressures remains an important method
for the diagnosis and treatment prognostication for patients with heart
failure. Although cardiac catheterization is the gold standard for estimating
central hemodynamic pressures, it is an invasive procedure that carries
inherent risks, making it a potentially dangerous procedure for some patients.
Approaches that leverage non-invasive signals - such as electrocardiogram (ECG)
- have the promise to make the routine estimation of cardiac pressures feasible
in both inpatient and outpatient settings. Prior models trained to estimate
intracardiac pressures (e.g., mean pulmonary capillary wedge pressure (mPCWP))
in a supervised fashion have shown good discriminatory ability but have been
limited to the labeled dataset from the heart failure cohort. To address this
issue and build a robust representation, we apply deep metric learning (DML)
and propose a novel self-supervised DML with distance-based mining that
improves the performance of a model with limited labels. We use a dataset that
contains over 5.4 million ECGs without concomitant central pressure labels to
pre-train a self-supervised DML model which showed improved classification of
elevated mPCWP compared to self-supervised contrastive baselines. Additionally,
the supervised DML model that uses ECGs with access to 8,172 mPCWP labels
demonstrated significantly better performance on the mPCWP regression task
compared to the supervised baseline. Moreover, our data suggest that DML yields
models that are performant across patient subgroups, even when some patient
subgroups are under-represented in the dataset. Our code is available at
https://github.com/mandiehyewon/ssldm
Learning to Measure: Distance Metric Learning with Structured Sparsity
Many important machine learning and data mining algorithms rely on a measure to provide a notion of distance or dissimilarity. Naive metrics such as the Euclidean distance are incapable of leveraging task-specific information, and consider all features as equal. A learned distance metric can become much more effective by honing in on structure specific to a task. Additionally, it is often extremely desirable for a metric to be sparse, as this vastly increases the ability to interpret or explain the measures produced by the distance metric. In this dissertation, we explore several current problems in distance metric learning and put forth solutions which make use of structured sparsity. The contributions of this dissertation may be broadly divided into two portions. In the first portion (chapter 2) we begin with a classic approach in distance metric learning and address a scenario where distance metric learning is typically inapplicable, i.e., the case of learning on heterogeneous data in a high-dimensional input space. We construct a projection-free distance metric learning algorithm which utilizes structured sparse updates and successfully demonstrate its application to learn a metric with over a billion parameters. The second portion (chapters 3 & 4) of this dissertation focuses on a new and intriguing regression-based approach to distance metric learning. Under this regression approach there are two sets of parameters to learn; those which parameterize the metric, and those defining the so-called ``virtual points''. We begin with an exploration of the metric parameterization and develop a structured sparse approach to robustify the metric to noisy, corrupted, or irrelevant data. We then focus on the virtual points and develop a new method for learning the metric and constraints together in a simultaneous manner. We demonstrate through empirical means that our approach results in a distance metric which is much more effective than the current state of-the-art. Machine learning algorithms have recently become ingrained in an incredibly diverse amount of technology. The primary focus of this dissertation is to develop more effective techniques to learn a distance metric. We believe that this work has the potential for rather broad-reaching impacts, as learning a more effective metric typically results in more accurate metric-based machine learning algorithms