23 research outputs found
cOOpD: Reformulating COPD classification on chest CT scans as anomaly detection using contrastive representations
Classification of heterogeneous diseases is challenging due to their
complexity, variability of symptoms and imaging findings. Chronic Obstructive
Pulmonary Disease (COPD) is a prime example, being underdiagnosed despite being
the third leading cause of death. Its sparse, diffuse and heterogeneous
appearance on computed tomography challenges supervised binary classification.
We reformulate COPD binary classification as an anomaly detection task,
proposing cOOpD: heterogeneous pathological regions are detected as
Out-of-Distribution (OOD) from normal homogeneous lung regions. To this end, we
learn representations of unlabeled lung regions employing a self-supervised
contrastive pretext model, potentially capturing specific characteristics of
diseased and healthy unlabeled regions. A generative model then learns the
distribution of healthy representations and identifies abnormalities (stemming
from COPD) as deviations. Patient-level scores are obtained by aggregating
region OOD scores. We show that cOOpD achieves the best performance on two
public datasets, with an increase of 8.2% and 7.7% in terms of AUROC compared
to the previous supervised state-of-the-art. Additionally, cOOpD yields
well-interpretable spatial anomaly maps and patient-level scores which we show
to be of additional value in identifying individuals in the early stage of
progression. Experiments in artificially designed real-world prevalence
settings further support that anomaly detection is a powerful way of tackling
COPD classification
Topology-Matching Normalizing Flows for Out-of-Distribution Detection in Robot Learning
To facilitate reliable deployments of autonomous robots in the real world, Out-of-Distribution (OOD) detection capabilities are often required. A powerful approach for OOD detection is based on density estimation with Normalizing Flows (NFs). However, we find that prior work with NFs attempts to match the complex target distribution topologically with naive base distributions leading to adverse implications. In this work, we circumvent this topological mismatch using an expressive class-conditional base distribution trained with an information-theoretic objective to match the required topology. The proposed method enjoys the merits of wide compatibility with existing learned models without any performance degradation and minimum computation overhead while enhancing OOD detection capabilities. We demonstrate superior results in density estimation and 2D object detection benchmarks in comparison with extensive baselines. Moreover, we showcase the applicability of the method with a real-robot deployment
Topology-Matching Normalizing Flows for Out-of-Distribution Detection in Robot Learning
To facilitate reliable deployments of autonomous robots in the real world,
Out-of-Distribution (OOD) detection capabilities are often required. A powerful
approach for OOD detection is based on density estimation with Normalizing
Flows (NFs). However, we find that prior work with NFs attempts to match the
complex target distribution topologically with naive base distributions leading
to adverse implications. In this work, we circumvent this topological mismatch
using an expressive class-conditional base distribution trained with an
information-theoretic objective to match the required topology. The proposed
method enjoys the merits of wide compatibility with existing learned models
without any performance degradation and minimum computation overhead while
enhancing OOD detection capabilities. We demonstrate superior results in
density estimation and 2D object detection benchmarks in comparison with
extensive baselines. Moreover, we showcase the applicability of the method with
a real-robot deployment.Comment: Accepted on CoRL202
Managing the unknown: a survey on Open Set Recognition and tangential areas
In real-world scenarios classification models are often required to perform
robustly when predicting samples belonging to classes that have not appeared
during its training stage. Open Set Recognition addresses this issue by
devising models capable of detecting unknown classes from samples arriving
during the testing phase, while maintaining a good level of performance in the
classification of samples belonging to known classes. This review
comprehensively overviews the recent literature related to Open Set
Recognition, identifying common practices, limitations, and connections of this
field with other machine learning research areas, such as continual learning,
out-of-distribution detection, novelty detection, and uncertainty estimation.
Our work also uncovers open problems and suggests several research directions
that may motivate and articulate future efforts towards more safe Artificial
Intelligence methods.Comment: 35 pages, 1 figure, 1 tabl
LMC: Large Model Collaboration with Cross-assessment for Training-Free Open-Set Object Recognition
Open-set object recognition aims to identify if an object is from a class
that has been encountered during training or not. To perform open-set object
recognition accurately, a key challenge is how to reduce the reliance on
spurious-discriminative features. In this paper, motivated by that different
large models pre-trained through different paradigms can possess very rich
while distinct implicit knowledge, we propose a novel framework named Large
Model Collaboration (LMC) to tackle the above challenge via collaborating
different off-the-shelf large models in a training-free manner. Moreover, we
also incorporate the proposed framework with several novel designs to
effectively extract implicit knowledge from large models. Extensive experiments
demonstrate the efficacy of our proposed framework. Code is available
\href{https://github.com/Harryqu123/LMC}{here}.Comment: NeurIPS 202
Embracing Limited and Imperfect Data: A Review on Plant Stress Recognition Using Deep Learning
Plant stress recognition has witnessed significant improvements in recent
years with the advent of deep learning. A large-scale and annotated training
dataset is required to achieve decent performance; however, collecting it is
frequently difficult and expensive. Therefore, deploying current deep
learning-based methods in real-world applications may suffer primarily from
limited and imperfect data. Embracing them is a promising strategy that has not
received sufficient attention. From this perspective, a systematic survey was
conducted in this study, with the ultimate objective of monitoring plant growth
by implementing deep learning, which frees humans and potentially reduces the
resultant losses from plant stress. We believe that our paper has highlighted
the importance of embracing this limited and imperfect data and enhanced its
relevant understanding