7,078 research outputs found
Diverse Target and Contribution Scheduling for Domain Generalization
Generalization under the distribution shift has been a great challenge in
computer vision. The prevailing practice of directly employing the one-hot
labels as the training targets in domain generalization~(DG) can lead to
gradient conflicts, making it insufficient for capturing the intrinsic class
characteristics and hard to increase the intra-class variation. Besides,
existing methods in DG mostly overlook the distinct contributions of source
(seen) domains, resulting in uneven learning from these domains. To address
these issues, we firstly present a theoretical and empirical analysis of the
existence of gradient conflicts in DG, unveiling the previously unexplored
relationship between distribution shifts and gradient conflicts during the
optimization process. In this paper, we present a novel perspective of DG from
the empirical source domain's risk and propose a new paradigm for DG called
Diverse Target and Contribution Scheduling (DTCS). DTCS comprises two
innovative modules: Diverse Target Supervision (DTS) and Diverse Contribution
Balance (DCB), with the aim of addressing the limitations associated with the
common utilization of one-hot labels and equal contributions for source domains
in DG. In specific, DTS employs distinct soft labels as training targets to
account for various feature distributions across domains and thereby mitigates
the gradient conflicts, and DCB dynamically balances the contributions of
source domains by ensuring a fair decline in losses of different source
domains. Extensive experiments with analysis on four benchmark datasets show
that the proposed method achieves a competitive performance in comparison with
the state-of-the-art approaches, demonstrating the effectiveness and advantages
of the proposed DTCS
Addressing Model Vulnerability to Distributional Shifts over Image Transformation Sets
We are concerned with the vulnerability of computer vision models to
distributional shifts. We formulate a combinatorial optimization problem that
allows evaluating the regions in the image space where a given model is more
vulnerable, in terms of image transformations applied to the input, and face it
with standard search algorithms. We further embed this idea in a training
procedure, where we define new data augmentation rules according to the image
transformations that the current model is most vulnerable to, over iterations.
An empirical evaluation on classification and semantic segmentation problems
suggests that the devised algorithm allows to train models that are more robust
against content-preserving image manipulations and, in general, against
distributional shifts.Comment: ICCV 2019 (camera ready
Wasserstein Distance Guided Representation Learning for Domain Adaptation
Domain adaptation aims at generalizing a high-performance learner on a target
domain via utilizing the knowledge distilled from a source domain which has a
different but related data distribution. One solution to domain adaptation is
to learn domain invariant feature representations while the learned
representations should also be discriminative in prediction. To learn such
representations, domain adaptation frameworks usually include a domain
invariant representation learning approach to measure and reduce the domain
discrepancy, as well as a discriminator for classification. Inspired by
Wasserstein GAN, in this paper we propose a novel approach to learn domain
invariant feature representations, namely Wasserstein Distance Guided
Representation Learning (WDGRL). WDGRL utilizes a neural network, denoted by
the domain critic, to estimate empirical Wasserstein distance between the
source and target samples and optimizes the feature extractor network to
minimize the estimated Wasserstein distance in an adversarial manner. The
theoretical advantages of Wasserstein distance for domain adaptation lie in its
gradient property and promising generalization bound. Empirical studies on
common sentiment and image classification adaptation datasets demonstrate that
our proposed WDGRL outperforms the state-of-the-art domain invariant
representation learning approaches.Comment: The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI
2018
Domain Generalization by Solving Jigsaw Puzzles
Human adaptability relies crucially on the ability to learn and merge
knowledge both from supervised and unsupervised learning: the parents point out
few important concepts, but then the children fill in the gaps on their own.
This is particularly effective, because supervised learning can never be
exhaustive and thus learning autonomously allows to discover invariances and
regularities that help to generalize. In this paper we propose to apply a
similar approach to the task of object recognition across domains: our model
learns the semantic labels in a supervised fashion, and broadens its
understanding of the data by learning from self-supervised signals how to solve
a jigsaw puzzle on the same images. This secondary task helps the network to
learn the concepts of spatial correlation while acting as a regularizer for the
classification task. Multiple experiments on the PACS, VLCS, Office-Home and
digits datasets confirm our intuition and show that this simple method
outperforms previous domain generalization and adaptation solutions. An
ablation study further illustrates the inner workings of our approach.Comment: Accepted at CVPR 2019 (oral
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