EnfoMax: Domain Entropy and Mutual Information Maximization for Domain Generalized Face Anti-spoofing

• URL: http://arxiv.org/abs/2302.08674v2
• Date: Sun, 4 Jun 2023 11:28:48 GMT
• Title: EnfoMax: Domain Entropy and Mutual Information Maximization for Domain Generalized Face Anti-spoofing
• Authors: Tianyi Zheng
• Abstract summary: Face anti-spoofing (FAS) method performs well under intra-domain setups. The domain generalization (DG) method has gained more attention in FAS. This paper proposes the EnfoMax framework, which uses information theory to analyze cross-domain FAS tasks.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The face anti-spoofing (FAS) method performs well under intra-domain setups. However, its cross-domain performance is unsatisfactory. As a result, the domain generalization (DG) method has gained more attention in FAS. Existing methods treat FAS as a simple binary classification task and propose a heuristic training objective to learn domain-invariant features. However, there is no theoretical explanation of what a domain-invariant feature is. Additionally, the lack of theoretical support makes domain generalization techniques such as adversarial training lack training stability. To address these issues, this paper proposes the EnfoMax framework, which uses information theory to analyze cross-domain FAS tasks. This framework provides theoretical guarantees and optimization objectives for domain-generalized FAS tasks. EnfoMax maximizes the domain entropy and mutual information of live samples in source domains without using adversarial learning. Experimental results demonstrate that our approach performs well on extensive public datasets and outperforms state-of-the-art methods.

Related papers

• Domain Generalization via Causal Adjustment for Cross-Domain Sentiment Analysis [59.73582306457387]
  We focus on the problem of domain generalization for cross-domain sentiment analysis. We propose a backdoor adjustment-based causal model to disentangle the domain-specific and domain-invariant representations. A series of experiments show the great performance and robustness of our model.
  arXiv  Detail & Related papers  (2024-02-22T13:26:56Z)
• Hypernetwork-Driven Model Fusion for Federated Domain Generalization [26.492360039272942]
  Federated Learning (FL) faces significant challenges with domain shifts in heterogeneous data. We propose a robust framework, coined as hypernetwork-based Federated Fusion (hFedF), using hypernetworks for non-linear aggregation. Our method employs client-specific embeddings and gradient alignment techniques to manage domain generalization effectively.
  arXiv  Detail & Related papers  (2024-02-10T15:42:03Z)
• Adaptive Mixture of Experts Learning for Generalizable Face Anti-Spoofing [37.75738807247752]
  Face anti-spoofing approaches based on domain generalization (DG) have drawn growing attention. Existing DG-based FAS approaches always capture the domain-invariant features for generalizing on the various unseen domains. We propose an Adaptive Mixture of Experts Learning framework, which exploits the domain-specific information to adaptively establish the link among the seen source domains and unseen target domains.
  arXiv  Detail & Related papers  (2022-07-20T13:02:51Z)
• Generalizing to Unseen Domains with Wasserstein Distributional Robustness under Limited Source Knowledge [22.285156929279207]
  Domain generalization aims at learning a universal model that performs well on unseen target domains. We propose a novel domain generalization framework called Wasserstein Distributionally Robust Domain Generalization (WDRDG)
  arXiv  Detail & Related papers  (2022-07-11T14:46:50Z)
• Decompose to Adapt: Cross-domain Object Detection via Feature Disentanglement [79.2994130944482]
  We design a Domain Disentanglement Faster-RCNN (DDF) to eliminate the source-specific information in the features for detection task learning. Our DDF method facilitates the feature disentanglement at the global and local stages, with a Global Triplet Disentanglement (GTD) module and an Instance Similarity Disentanglement (ISD) module. By outperforming state-of-the-art methods on four benchmark UDA object detection tasks, our DDF method is demonstrated to be effective with wide applicability.
  arXiv  Detail & Related papers  (2022-01-06T05:43:01Z)
• f-Domain-Adversarial Learning: Theory and Algorithms [82.97698406515667]
  Unsupervised domain adaptation is used in many machine learning applications where, during training, a model has access to unlabeled data in the target domain. We derive a novel generalization bound for domain adaptation that exploits a new measure of discrepancy between distributions based on a variational characterization of f-divergences.
  arXiv  Detail & Related papers  (2021-06-21T18:21:09Z)
• Generalizable Representation Learning for Mixture Domain Face Anti-Spoofing [53.82826073959756]
  Face anti-spoofing approach based on domain generalization(DG) has drawn growing attention due to its robustness forunseen scenarios. We propose domain dy-namic adjustment meta-learning (D2AM) without using do-main labels. To overcome the limitation, we propose domain dy-namic adjustment meta-learning (D2AM) without using do-main labels.
  arXiv  Detail & Related papers  (2021-05-06T06:04:59Z)
• Model-Based Domain Generalization [96.84818110323518]
  We propose a novel approach for the domain generalization problem called Model-Based Domain Generalization. Our algorithms beat the current state-of-the-art methods on the very-recently-proposed WILDS benchmark by up to 20 percentage points.
  arXiv  Detail & Related papers  (2021-02-23T00:59:02Z)
• Unsupervised Cross-domain Image Classification by Distance Metric Guided Feature Alignment [11.74643883335152]
  Unsupervised domain adaptation is a promising avenue which transfers knowledge from a source domain to a target domain. We propose distance metric guided feature alignment (MetFA) to extract discriminative as well as domain-invariant features on both source and target domains. Our model integrates class distribution alignment to transfer semantic knowledge from a source domain to a target domain.
  arXiv  Detail & Related papers  (2020-08-19T13:36:57Z)
• Domain Conditioned Adaptation Network [90.63261870610211]
  We propose a Domain Conditioned Adaptation Network (DCAN) to excite distinct convolutional channels with a domain conditioned channel attention mechanism. This is the first work to explore the domain-wise convolutional channel activation for deep DA networks.
  arXiv  Detail & Related papers  (2020-05-14T04:23:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.