5 research outputs found
NAPA-VQ: Neighborhood Aware Prototype Augmentation with Vector Quantization for Continual Learning
Catastrophic forgetting; the loss of old knowledge upon acquiring new
knowledge, is a pitfall faced by deep neural networks in real-world
applications. Many prevailing solutions to this problem rely on storing
exemplars (previously encountered data), which may not be feasible in
applications with memory limitations or privacy constraints. Therefore, the
recent focus has been on Non-Exemplar based Class Incremental Learning (NECIL)
where a model incrementally learns about new classes without using any past
exemplars. However, due to the lack of old data, NECIL methods struggle to
discriminate between old and new classes causing their feature representations
to overlap. We propose NAPA-VQ: Neighborhood Aware Prototype Augmentation with
Vector Quantization, a framework that reduces this class overlap in NECIL. We
draw inspiration from Neural Gas to learn the topological relationships in the
feature space, identifying the neighboring classes that are most likely to get
confused with each other. This neighborhood information is utilized to enforce
strong separation between the neighboring classes as well as to generate old
class representative prototypes that can better aid in obtaining a
discriminative decision boundary between old and new classes. Our comprehensive
experiments on CIFAR-100, TinyImageNet, and ImageNet-Subset demonstrate that
NAPA-VQ outperforms the State-of-the-art NECIL methods by an average
improvement of 5%, 2%, and 4% in accuracy and 10%, 3%, and 9% in forgetting
respectively. Our code can be found in https://github.com/TamashaM/NAPA-VQ.git.Comment: Accepted to ICCV 202
DALLE-URBAN: Capturing the urban design expertise of large text to image transformers
Automatically converting text descriptions into images using transformer
architectures has recently received considerable attention. Such advances have
implications for many applied design disciplines across fashion, art,
architecture, urban planning, landscape design and the future tools available
to such disciplines. However, a detailed analysis capturing the capabilities of
such models, specifically with a focus on the built environment, has not been
performed to date. In this work, we investigate the capabilities and biases of
such text-to-image methods as it applies to the built environment in detail. We
use a systematic grammar to generate queries related to the built environment
and evaluate resulting generated images. We generate 1020 different images and
find that text to image transformers are robust at generating realistic images
across different domains for this use-case. Generated imagery can be found at
the github: https://github.com/sachith500/DALLEURBANComment: Accepted to DICTA 2022, released 11000+ environmental scene images
generated by Stable Diffusion and 1000+ images generated by DALLE-
ENVirT: inference of ecological characteristics of viruses from metagenomic data
Background
Estimating the parameters that describe the ecology of viruses,particularly those that are novel, can be made possible using metagenomic approaches. However, the best-performing existing methods require databases to first estimate an average genome length of a viral community before being able to estimate other parameters, such as viral richness. Although this approach has been widely used, it can adversely skew results since the majority of viruses are yet to be catalogued in databases.
Results
In this paper, we present ENVirT, a method for estimating the richness of novel viral mixtures, and for the first time we also show that it is possible to simultaneously estimate the average genome length without a priori information. This is shown to be a significant improvement over database-dependent methods, since we can now robustly analyze samples that may include novel viral types under-represented in current databases. We demonstrate that the viral richness estimates produced by ENVirT are several orders of magnitude higher in accuracy than the estimates produced by existing methods named PHACCS and CatchAll when benchmarked against simulated data. We repeated the analysis of 20 metavirome samples using ENVirT, which produced results in close agreement with complementary in virto analyses.
Conclusions
These insights were previously not captured by existing computational methods. As such, ENVirT is shown to be an essential tool for enhancing our understanding of novel viral populations.This work was supported partially by Australia Research Council [grant
numbers LP140100670 and DP150103512] and the Biodiversity Research
Center, Academia Sinica, Taiwan. DJ, DH, DS and YS were funded by the MIFRS
and MIRS scholarships of The University of Melbourne. Publication costs were
funded by The Australian National University
Exploratory Analysis of Highly Dimensional Data: Parametric Methods for Dimensionality Reduction, Visualization and Feature Extraction with Applications in Computational Biology
© 2020 Damith Asanka SenanayakeRecent advances in experimental technologies have facilitated the gathering of data with thousands of variables. Because of this, modern data analysis tasks often encounter high dimensional data, which are challenging to analyse. Such analysis is made more difficult with the lack of ground-truth. In this thesis, I have explored two aspects of high-dimensional exploratory data analysis: 1) Dimensionality Reduction and Visualization of high-dimensional data to gain insights into the structure of the data and, 2) Extraction and interpretation of feature subsets (motifs) which explain the structure of high-dimensional data. I have presented methods that are built on concepts of Neural Networks - a powerful modern branch of optimization techniques. Through my work, I have shown that using relatively infrequently used variants of neural networks and building on their concepts (e.g. vector quantization and Hebbian Learning) we can produce powerful dimensionality reduction methods that address major gap areas of research. I have further shown that using unsupervised deep neural networks with contextual regularization, we can produce a framework to extract minimal motifs that optimally reconstruct complex nonlinear structures present in high dimensional data
Classification of Fracture Risk in Fallers Using DualâEnergy XâRay Absorptiometry (DXA) Images and Deep LearningâBased Feature Extraction
Abstract Dualâenergy Xâray absorptiometry (DXA) scans are one of the most frequently used imaging techniques for calculating bone mineral density, yet calculating fracture risk using DXA image features is rarely performed. The objective of this study was to combine deep neural networks, together with DXA images and patient clinical information, to evaluate fracture risk in a cohort of adults with at least one known fall and ageâmatched healthy controls. DXA images of the entire body as, well as isolated images of the hip, forearm, and spine (1488 total), were obtained from 478 fallers and 48 nonâfaller controls. A modeling pipeline was developed for fracture risk prediction using the DXA images and clinical data. First, selfâsupervised pretraining of feature extractors was performed using a small vision transformer (ViTâS) and a convolutional neural network model (VGGâ16 and Resnetâ50). After pretraining, the feature extractors were then paired with a multilayer perceptron model, which was used for fracture risk classification. Classification was achieved with an average area under the receiverâoperating characteristic curve (AUROC) score of 74.3%. This study demonstrates ViTâS as a promising neural network technique for fracture risk classification using DXA scans. The findings have future application as a fracture risk screening tool for older adults at risk of falls. © 2023 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research