2,495 research outputs found
Channel selection for test-time adaptation under distribution shift
To ensure robustness and generalization to real-world scenarios, test-time adaptation has been recently studied as an approach to adjust models to a new data
distribution during inference. Test-time batch normalization is a simple and popular
method that achieved compelling performance on domain shift benchmarks by
recalculating batch normalization statistics on test batches. However, in many
practical applications this technique is vulnerable to label distribution shifts. We
propose to tackle this challenge by only selectively adapting channels in a deep
network, minimizing drastic adaptation that is sensitive to label shifts. We find that
adapted models significantly improve the performance compared to the baseline
models and counteract unknown label shifts
On Sensitivity and Robustness of Normalization Schemes to Input Distribution Shifts in Automatic MR Image Diagnosis
Magnetic Resonance Imaging (MRI) is considered the gold standard of medical
imaging because of the excellent soft-tissue contrast exhibited in the images
reconstructed by the MRI pipeline, which in-turn enables the human radiologist
to discern many pathologies easily. More recently, Deep Learning (DL) models
have also achieved state-of-the-art performance in diagnosing multiple diseases
using these reconstructed images as input. However, the image reconstruction
process within the MRI pipeline, which requires the use of complex hardware and
adjustment of a large number of scanner parameters, is highly susceptible to
noise of various forms, resulting in arbitrary artifacts within the images.
Furthermore, the noise distribution is not stationary and varies within a
machine, across machines, and patients, leading to varying artifacts within the
images. Unfortunately, DL models are quite sensitive to these varying artifacts
as it leads to changes in the input data distribution between the training and
testing phases. The lack of robustness of these models against varying
artifacts impedes their use in medical applications where safety is critical.
In this work, we focus on improving the generalization performance of these
models in the presence of multiple varying artifacts that manifest due to the
complexity of the MR data acquisition. In our experiments, we observe that
Batch Normalization, a widely used technique during the training of DL models
for medical image analysis, is a significant cause of performance degradation
in these changing environments. As a solution, we propose to use other
normalization techniques, such as Group Normalization and Layer Normalization
(LN), to inject robustness into model performance against varying image
artifacts. Through a systematic set of experiments, we show that GN and LN
provide better accuracy for various MR artifacts and distribution shifts.Comment: Accepted at MIDL 202
Evaluating Continual Test-Time Adaptation for Contextual and Semantic Domain Shifts
In this paper, our goal is to adapt a pre-trained convolutional neural
network to domain shifts at test time. We do so continually with the incoming
stream of test batches, without labels. The existing literature mostly operates
on artificial shifts obtained via adversarial perturbations of a test image.
Motivated by this, we evaluate the state of the art on two realistic and
challenging sources of domain shifts, namely contextual and semantic shifts.
Contextual shifts correspond to the environment types, for example, a model
pre-trained on indoor context has to adapt to the outdoor context on CORe-50.
Semantic shifts correspond to the capture types, for example a model
pre-trained on natural images has to adapt to cliparts, sketches, and paintings
on DomainNet. We include in our analysis recent techniques such as
Prediction-Time Batch Normalization (BN), Test Entropy Minimization (TENT) and
Continual Test-Time Adaptation (CoTTA). Our findings are three-fold: i)
Test-time adaptation methods perform better and forget less on contextual
shifts compared to semantic shifts, ii) TENT outperforms other methods on
short-term adaptation, whereas CoTTA outpeforms other methods on long-term
adaptation, iii) BN is most reliable and robust. Our code is available at
https://github.com/tommiekerssies/Evaluating-Continual-Test-Time-Adaptation-for-Contextual-and-Semantic-Domain-Shifts
Towards Bridging the gap between Empirical and Certified Robustness against Adversarial Examples
The current state-of-the-art defense methods against adversarial examples
typically focus on improving either empirical or certified robustness. Among
them, adversarially trained (AT) models produce empirical state-of-the-art
defense against adversarial examples without providing any robustness
guarantees for large classifiers or higher-dimensional inputs. In contrast,
existing randomized smoothing based models achieve state-of-the-art certified
robustness while significantly degrading the empirical robustness against
adversarial examples. In this paper, we propose a novel method, called
\emph{Certification through Adaptation}, that transforms an AT model into a
randomized smoothing classifier during inference to provide certified
robustness for norm without affecting their empirical robustness
against adversarial attacks. We also propose \emph{Auto-Noise} technique that
efficiently approximates the appropriate noise levels to flexibly certify the
test examples using randomized smoothing technique. Our proposed
\emph{Certification through Adaptation} with \emph{Auto-Noise} technique
achieves an \textit{average certified radius (ACR) scores} up to and
respectively for CIFAR-10 and ImageNet datasets using AT models without
affecting their empirical robustness or benign accuracy. Therefore, our paper
is a step towards bridging the gap between the empirical and certified
robustness against adversarial examples by achieving both using the same
classifier.Comment: An abridged version of this work has been presented at ICLR 2021
Workshop on Security and Safety in Machine Learning Systems:
https://aisecure-workshop.github.io/aml-iclr2021/papers/2.pd
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