15 research outputs found
A Hybrid Architecture for Out of Domain Intent Detection and Intent Discovery
Intent Detection is one of the tasks of the Natural Language Understanding
(NLU) unit in task-oriented dialogue systems. Out of Scope (OOS) and Out of
Domain (OOD) inputs may run these systems into a problem. On the other side, a
labeled dataset is needed to train a model for Intent Detection in
task-oriented dialogue systems. The creation of a labeled dataset is
time-consuming and needs human resources. The purpose of this article is to
address mentioned problems. The task of identifying OOD/OOS inputs is named
OOD/OOS Intent Detection. Also, discovering new intents and pseudo-labeling of
OOD inputs is well known by Intent Discovery. In OOD intent detection part, we
make use of a Variational Autoencoder to distinguish between known and unknown
intents independent of input data distribution. After that, an unsupervised
clustering method is used to discover different unknown intents underlying
OOD/OOS inputs. We also apply a non-linear dimensionality reduction on OOD/OOS
representations to make distances between representations more meaning full for
clustering. Our results show that the proposed model for both OOD/OOS Intent
Detection and Intent Discovery achieves great results and passes baselines in
English and Persian languages
SR-OOD: Out-of-Distribution Detection via Sample Repairing
It is widely reported that deep generative models can classify
out-of-distribution (OOD) samples as in-distribution with high confidence. In
this work, we propose a hypothesis that this phenomenon is due to the
reconstruction task, which can cause the generative model to focus too much on
low-level features and not enough on semantic information. To address this
issue, we introduce SR-OOD, an OOD detection framework that utilizes sample
repairing to encourage the generative model to learn more than just an identity
map. By focusing on semantics, our framework improves OOD detection performance
without external data and label information. Our experimental results
demonstrate the competitiveness of our approach in detecting OOD samples
Detecting Backdoors in Neural Networks Using Novel Feature-Based Anomaly Detection
This paper proposes a new defense against neural network backdooring attacks
that are maliciously trained to mispredict in the presence of attacker-chosen
triggers. Our defense is based on the intuition that the feature extraction
layers of a backdoored network embed new features to detect the presence of a
trigger and the subsequent classification layers learn to mispredict when
triggers are detected. Therefore, to detect backdoors, the proposed defense
uses two synergistic anomaly detectors trained on clean validation data: the
first is a novelty detector that checks for anomalous features, while the
second detects anomalous mappings from features to outputs by comparing with a
separate classifier trained on validation data. The approach is evaluated on a
wide range of backdoored networks (with multiple variations of triggers) that
successfully evade state-of-the-art defenses. Additionally, we evaluate the
robustness of our approach on imperceptible perturbations, scalability on
large-scale datasets, and effectiveness under domain shift. This paper also
shows that the defense can be further improved using data augmentation
SAFE: Sensitivity-Aware Features for Out-of-Distribution Object Detection
We address the problem of out-of-distribution (OOD) detection for the task of
object detection. We show that residual convolutional layers with batch
normalisation produce Sensitivity-Aware FEatures (SAFE) that are consistently
powerful for distinguishing in-distribution from out-of-distribution
detections. By extracting SAFE vectors for every detected object, and training
a multilayer perceptron on the surrogate task of distinguishing adversarially
perturbed from clean in-distribution examples, we circumvent the need for
realistic OOD training data, computationally expensive generative models, or
retraining of the base object detector. SAFE outperforms the state-of-the-art
OOD object detectors on multiple benchmarks by large margins, e.g. reducing the
FPR95 by an absolute 30.6% from 48.3% to 17.7% on the OpenImages dataset
A Functional Data Perspective and Baseline On Multi-Layer Out-of-Distribution Detection
A key feature of out-of-distribution (OOD) detection is to exploit a trained
neural network by extracting statistical patterns and relationships through the
multi-layer classifier to detect shifts in the expected input data
distribution. Despite achieving solid results, several state-of-the-art methods
rely on the penultimate or last layer outputs only, leaving behind valuable
information for OOD detection. Methods that explore the multiple layers either
require a special architecture or a supervised objective to do so. This work
adopts an original approach based on a functional view of the network that
exploits the sample's trajectories through the various layers and their
statistical dependencies. It goes beyond multivariate features aggregation and
introduces a baseline rooted in functional anomaly detection. In this new
framework, OOD detection translates into detecting samples whose trajectories
differ from the typical behavior characterized by the training set. We validate
our method and empirically demonstrate its effectiveness in OOD detection
compared to strong state-of-the-art baselines on computer vision benchmarks
Bayes-TrEx: a Bayesian Sampling Approach to Model Transparency by Example
Post-hoc explanation methods are gaining popularity for interpreting,
understanding, and debugging neural networks. Most analyses using such methods
explain decisions in response to inputs drawn from the test set. However, the
test set may have few examples that trigger some model behaviors, such as
high-confidence failures or ambiguous classifications. To address these
challenges, we introduce a flexible model inspection framework: Bayes-TrEx.
Given a data distribution, Bayes-TrEx finds in-distribution examples with a
specified prediction confidence. We demonstrate several use cases of
Bayes-TrEx, including revealing highly confident (mis)classifications,
visualizing class boundaries via ambiguous examples, understanding novel-class
extrapolation behavior, and exposing neural network overconfidence. We use
Bayes-TrEx to study classifiers trained on CLEVR, MNIST, and Fashion-MNIST, and
we show that this framework enables more flexible holistic model analysis than
just inspecting the test set. Code is available at
https://github.com/serenabooth/Bayes-TrEx.Comment: Accepted at AAAI 202