30 research outputs found
Deep Domain-Adversarial Image Generation for Domain Generalisation
Machine learning models typically suffer from the domain shift problem when
trained on a source dataset and evaluated on a target dataset of different
distribution. To overcome this problem, domain generalisation (DG) methods aim
to leverage data from multiple source domains so that a trained model can
generalise to unseen domains. In this paper, we propose a novel DG approach
based on \emph{Deep Domain-Adversarial Image Generation} (DDAIG). Specifically,
DDAIG consists of three components, namely a label classifier, a domain
classifier and a domain transformation network (DoTNet). The goal for DoTNet is
to map the source training data to unseen domains. This is achieved by having a
learning objective formulated to ensure that the generated data can be
correctly classified by the label classifier while fooling the domain
classifier. By augmenting the source training data with the generated unseen
domain data, we can make the label classifier more robust to unknown domain
changes. Extensive experiments on four DG datasets demonstrate the
effectiveness of our approach.Comment: 8 page
Rethinking Domain Generalization Baselines
Despite being very powerful in standard learning settings, deep learning models can be extremely brittle when deployed in scenarios different from those on which they were trained. Domain generalization methods investigate this problem and data augmentation strategies have shown to be helpful tools to increase data variability, supporting model robustness across domains. In our work we focus on style transfer data augmentation and we present how it can be implemented with a simple and inexpensive strategy to improve generalization. Moreover, we analyze the behavior of current state of the art domain generalization methods when integrated with this augmentation solution: our thorough experimental evaluation shows that their original effect almost always disappears with respect to the augmented baseline. This issue open new scenarios for domain generalization research, highlighting the need of novel methods properly able to take advantage of the introduced data variability
Domain Similarity-Perceived Label Assignment for Domain Generalized Underwater Object Detection
The inherent characteristics and light fluctuations of water bodies give rise
to the huge difference between different layers and regions in underwater
environments. When the test set is collected in a different marine area from
the training set, the issue of domain shift emerges, significantly compromising
the model's ability to generalize. The Domain Adversarial Learning (DAL)
training strategy has been previously utilized to tackle such challenges.
However, DAL heavily depends on manually one-hot domain labels, which implies
no difference among the samples in the same domain. Such an assumption results
in the instability of DAL. This paper introduces the concept of Domain
Similarity-Perceived Label Assignment (DSP). The domain label for each image is
regarded as its similarity to the specified domains. Through domain-specific
data augmentation techniques, we achieved state-of-the-art results on the
underwater cross-domain object detection benchmark S-UODAC2020. Furthermore, we
validated the effectiveness of our method in the Cityscapes dataset.Comment: 10 pages,7 figure
Privacy-Preserving Constrained Domain Generalization via Gradient Alignment
Deep neural networks (DNN) have demonstrated unprecedented success for
medical imaging applications. However, due to the issue of limited dataset
availability and the strict legal and ethical requirements for patient privacy
protection, the broad applications of medical imaging classification driven by
DNN with large-scale training data have been largely hindered. For example,
when training the DNN from one domain (e.g., with data only from one hospital),
the generalization capability to another domain (e.g., data from another
hospital) could be largely lacking. In this paper, we aim to tackle this
problem by developing the privacy-preserving constrained domain generalization
method, aiming to improve the generalization capability under the
privacy-preserving condition. In particular, We propose to improve the
information aggregation process on the centralized server-side with a novel
gradient alignment loss, expecting that the trained model can be better
generalized to the "unseen" but related medical images. The rationale and
effectiveness of our proposed method can be explained by connecting our
proposed method with the Maximum Mean Discrepancy (MMD) which has been widely
adopted as the distribution distance measurement. Experimental results on two
challenging medical imaging classification tasks indicate that our method can
achieve better cross-domain generalization capability compared to the
state-of-the-art federated learning methods
Discovery of New Multi-Level Features for Domain Generalization via Knowledge Corruption
Machine learning models that can generalize to unseen domains are essential
when applied in real-world scenarios involving strong domain shifts. We address
the challenging domain generalization (DG) problem, where a model trained on a
set of source domains is expected to generalize well in unseen domains without
any exposure to their data. The main challenge of DG is that the features
learned from the source domains are not necessarily present in the unseen
target domains, leading to performance deterioration. We assume that learning a
richer set of features is crucial to improve the transfer to a wider set of
unknown domains. For this reason, we propose COLUMBUS, a method that enforces
new feature discovery via a targeted corruption of the most relevant input and
multi-level representations of the data. We conduct an extensive empirical
evaluation to demonstrate the effectiveness of the proposed approach which
achieves new state-of-the-art results by outperforming 18 DG algorithms on
multiple DG benchmark datasets in the DomainBed framework.Comment: Accepted at AAAI 2022 (AIBSD Workshop) and ICPR 202
TFS-ViT: Token-Level Feature Stylization for Domain Generalization
Standard deep learning models such as convolutional neural networks (CNNs)
lack the ability of generalizing to domains which have not been seen during
training. This problem is mainly due to the common but often wrong assumption
of such models that the source and target data come from the same i.i.d.
distribution. Recently, Vision Transformers (ViTs) have shown outstanding
performance for a broad range of computer vision tasks. However, very few
studies have investigated their ability to generalize to new domains. This
paper presents a first Token-level Feature Stylization (TFS-ViT) approach for
domain generalization, which improves the performance of ViTs to unseen data by
synthesizing new domains. Our approach transforms token features by mixing the
normalization statistics of images from different domains. We further improve
this approach with a novel strategy for attention-aware stylization, which uses
the attention maps of class (CLS) tokens to compute and mix normalization
statistics of tokens corresponding to different image regions. The proposed
method is flexible to the choice of backbone model and can be easily applied to
any ViT-based architecture with a negligible increase in computational
complexity. Comprehensive experiments show that our approach is able to achieve
state-of-the-art performance on five challenging benchmarks for domain
generalization, and demonstrate its ability to deal with different types of
domain shifts. The implementation is available at:
https://github.com/Mehrdad-Noori/TFS-ViT_Token-level_Feature_Stylization
Neuron Coverage-Guided Domain Generalization
This paper focuses on the domain generalization task where domain knowledge
is unavailable, and even worse, only samples from a single domain can be
utilized during training. Our motivation originates from the recent progresses
in deep neural network (DNN) testing, which has shown that maximizing neuron
coverage of DNN can help to explore possible defects of DNN (i.e.,
misclassification). More specifically, by treating the DNN as a program and
each neuron as a functional point of the code, during the network training we
aim to improve the generalization capability by maximizing the neuron coverage
of DNN with the gradient similarity regularization between the original and
augmented samples. As such, the decision behavior of the DNN is optimized,
avoiding the arbitrary neurons that are deleterious for the unseen samples, and
leading to the trained DNN that can be better generalized to
out-of-distribution samples. Extensive studies on various domain generalization
tasks based on both single and multiple domain(s) setting demonstrate the
effectiveness of our proposed approach compared with state-of-the-art baseline
methods. We also analyze our method by conducting visualization based on
network dissection. The results further provide useful evidence on the
rationality and effectiveness of our approach