9 research outputs found
A Global Model Approach to Robust Few-Shot SAR Automatic Target Recognition
In real-world scenarios, it may not always be possible to collect hundreds of
labeled samples per class for training deep learning-based SAR Automatic Target
Recognition (ATR) models. This work specifically tackles the few-shot SAR ATR
problem, where only a handful of labeled samples may be available to support
the task of interest. Our approach is composed of two stages. In the first, a
global representation model is trained via self-supervised learning on a large
pool of diverse and unlabeled SAR data. In the second stage, the global model
is used as a fixed feature extractor and a classifier is trained to partition
the feature space given the few-shot support samples, while simultaneously
being calibrated to detect anomalous inputs. Unlike competing approaches which
require a pristine labeled dataset for pretraining via meta-learning, our
approach learns highly transferable features from unlabeled data that have
little-to-no relation to the downstream task. We evaluate our method in
standard and extended MSTAR operating conditions and find it to achieve high
accuracy and robust out-of-distribution detection in many different few-shot
settings. Our results are particularly significant because they show the merit
of a global model approach to SAR ATR, which makes minimal assumptions, and
provides many axes for extendability
Self-Trained Proposal Networks for the Open World
Current state-of-the-art object proposal networks are trained with a
closed-world assumption, meaning they learn to only detect objects of the
training classes. These models fail to provide high recall in open-world
environments where important novel objects may be encountered. While a handful
of recent works attempt to tackle this problem, they fail to consider that the
optimal behavior of a proposal network can vary significantly depending on the
data and application. Our goal is to provide a flexible proposal solution that
can be easily tuned to suit a variety of open-world settings. To this end, we
design a Self-Trained Proposal Network (STPN) that leverages an adjustable
hybrid architecture, a novel self-training procedure, and dynamic loss
components to optimize the tradeoff between known and unknown object detection
performance. To thoroughly evaluate our method, we devise several new
challenges which invoke varying degrees of label bias by altering known class
diversity and label count. We find that in every task, STPN easily outperforms
existing baselines (e.g., RPN, OLN). Our method is also highly data efficient,
surpassing baseline recall with a fraction of the labeled data.Comment: 19 pages, 9 figures, 10 table
Establishing baselines and introducing TernaryMixOE for fine-grained out-of-distribution detection
Machine learning models deployed in the open world may encounter observations
that they were not trained to recognize, and they risk misclassifying such
observations with high confidence. Therefore, it is essential that these models
are able to ascertain what is in-distribution (ID) and out-of-distribution
(OOD), to avoid this misclassification. In recent years, huge strides have been
made in creating models that are robust to this distinction. As a result, the
current state-of-the-art has reached near perfect performance on relatively
coarse-grained OOD detection tasks, such as distinguishing horses from trucks,
while struggling with finer-grained classification, like differentiating models
of commercial aircraft. In this paper, we describe a new theoretical framework
for understanding fine- and coarse-grained OOD detection, we re-conceptualize
fine grained classification into a three part problem, and we propose a new
baseline task for OOD models on two fine-grained hierarchical data sets, two
new evaluation methods to differentiate fine- and coarse-grained OOD
performance, along with a new loss function for models in this task
Mixture Outlier Exposure for Out-of-Distribution Detection in Fine-grained Settings
Enabling out-of-distribution (OOD) detection for DNNs is critical for their
safe and reliable operation in the open world. Despite recent progress, current
works often consider a coarse level of granularity in the OOD problem, which
fail to approximate many real-world fine-grained tasks where high granularity
may be expected between the in-distribution (ID) data and the OOD data (e.g.,
identifying novel bird species for a bird classification system in the wild).
In this work, we start by carefully constructing four large-scale fine-grained
test environments in which existing methods are shown to have difficulties. We
find that current methods, including ones that include a large/diverse set of
outliers during DNN training, have poor coverage over the broad region where
fine-grained OOD samples locate. We then propose Mixture Outlier Exposure
(MixOE), which effectively expands the covered OOD region by mixing ID data and
training outliers, and regularizes the model behaviour by linearly decaying the
prediction confidence as the input transitions from ID to OOD. Extensive
experiments and analyses demonstrate the effectiveness of MixOE for improving
OOD detection in fine-grained settings