Published research highlights the presence of demographic bias in automated facial attribute classification.
The proposed bias mitigation techniques are mostly based on supervised learning, which requires a large amount of
labeled training data for generalizability and scalability. However, labeled data is limited, requires laborious annotation,
poses privacy risks, and can perpetuate human bias. In contrast, self-supervised learning (SSL) capitalizes
on freely available unlabeled data, rendering trained models more scalable and generalizable. However, these
label-free SSL models may also introduce biases by sampling false negative pairs, especially at low-data regimes
(< 200K images) under low compute settings. Further, SSL-based models may suffer from performance degradation
due to a lack of quality assurance of the unlabeled data sourced from the web. This paper proposes a fully self-supervised
pipeline for demographically fair facial attribute classifiers. Leveraging completely unlabeled data pseudolabeled
via pre-trained encoders, diverse data curation techniques, and meta-learning-based weighted contrastive learning, our method significantly outperforms existing SSL approaches proposed for downstream image classification tasks. Extensive evaluations on the FairFace and CelebA datasets demonstrate the efficacy of our pipeline in obtaining fair performance over existing baselines. Thus, setting a new benchmark for SSL in the fairness of facial attribute classification.
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Joint Self-Supervised Image-Volume Representation Learning with Intra-inter Contrastive Clustering
Collecting large-scale medical datasets with fully annotated samples for training of deep networks is prohibitively expensive, especially for 3D volume data. Recent breakthroughs in self-supervised learning (SSL) offer the ability to overcome the lack of labeled training samples by learning feature representations from unlabeled data. However, most current SSL techniques in the medical field have been designed for either 2D images or 3D volumes. In practice, this restricts the capability to fully leverage unlabeled data from numerous sources, which may include both 2D and 3D data. Additionally, the use of these pre-trained networks is constrained to downstream tasks with compatible data dimensions.In this paper, we propose a novel framework for unsupervised joint learning on 2D and 3D data modalities. Given a set of 2D images or 2D slices extracted from 3D volumes, we construct an SSL task based on a 2D contrastive clustering problem for distinct classes. The 3D volumes are exploited by computing vectored embedding at each slice and then assembling a holistic feature through deformable self-attention mechanisms in Transformer, allowing incorporating long-range dependencies between slices inside 3D volumes. These holistic features are further utilized to define a novel 3D clustering agreement-based SSL task and masking embedding prediction inspired by pre-trained language models. Experiments on downstream tasks, such as 3D brain segmentation, lung nodule detection, 3D heart structures segmentation, and abnormal chest X-ray detection, demonstrate the effectiveness of our joint 2D and 3D SSL approach. We improve plain 2D Deep-ClusterV2 and SwAV by a significant margin and also surpass various modern 2D and 3D SSL approaches.
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- Award ID(s):
- 2100237
- NSF-PAR ID:
- 10431877
- Date Published:
- Journal Name:
- Proceedings of the AAAI Conference on Artificial Intelligence
- Volume:
- 37
- Issue:
- 12
- ISSN:
- 2159-5399
- Page Range / eLocation ID:
- 14426 to 14435
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1609/aaai.v37i12.26687
