Self-supervised learning has achieved remarkable success in acquiring
high-quality representations from unlabeled data. The widely adopted
contrastive learning framework aims to learn invariant representations by
minimizing the distance between positive views originating from the same image.
However, existing techniques to construct positive views highly rely on manual
transformations, resulting in limited diversity and potentially false positive
pairs. To tackle these challenges, we present GenView, a controllable framework
that augments the diversity of positive views leveraging the power of
pretrained generative models while preserving semantics. We develop an adaptive
view generation method that dynamically adjusts the noise level in sampling to
ensure the preservation of essential semantic meaning while introducing
variability. Additionally, we introduce a quality-driven contrastive loss,
which assesses the quality of positive pairs by considering both foreground
similarity and background diversity. This loss prioritizes the high-quality
positive pairs we construct while reducing the influence of low-quality pairs,
thereby mitigating potential semantic inconsistencies introduced by generative
models and aggressive data augmentation. Thanks to the improved positive view
quality and the quality-driven contrastive loss, GenView significantly improves
self-supervised learning across various tasks. For instance, GenView improves
MoCov2 performance by 2.5%/2.2% on ImageNet linear/semi-supervised
classification. Moreover, GenView even performs much better than naively
augmenting the ImageNet dataset with Laion400M or ImageNet21K. Code is
available at this https URL

自监督学习通过从无标签数据中获取高质量的表示已经取得了显著的成功。GenView 是一个可控的框架，通过增加积极视角的多样性利用预训练生成模型的能力，同时保留语义。研究中引入了自适应视角生成方法来调整采样噪声水平，以确保保留基本语义意义并引入变异性。此外，引入了质量驱动的对比损失，通过考虑前景相似性和背景多样性评估正样本对的质量。GenView 明显改善了各种任务中的自监督学习性能，例如，在 ImageNet 线性 / 半监督分类上，GenView 将 MoCov2 的性能提高了 2.5％/2.2％。此外，与简单地使用 Laion400M 或 ImageNet21K 扩充 ImageNet 数据集相比，GenView 的性能更好。

GenView: 使用预训练生成模型提升自监督学习的视图质量

GenView: Enhancing View Quality with Pretrained Generative Model for  Self-Supervised Learning

Self-supervised graph representation learning has recently shown considerable
promise in a range of fields, including bioinformatics and social networks. A
large number of graph contrastive learning approaches have shown promising
performance for representation learning on graphs, which train models by
maximizing agreement between original graphs and their augmented views (i.e.,
positive views). Unfortunately, these methods usually involve pre-defined
augmentation strategies based on the knowledge of human experts. Moreover,
these strategies may fail to generate challenging positive views to provide
sufficient supervision signals. In this paper, we present a novel approach
named Graph Pooling ContraSt (GPS) to address these issues. Motivated by the
fact that graph pooling can adaptively coarsen the graph with the removal of
redundancy, we rethink graph pooling and leverage it to automatically generate
multi-scale positive views with varying emphasis on providing challenging
positives and preserving semantics, i.e., strongly-augmented view and
weakly-augmented view. Then, we incorporate both views into a joint contrastive
learning framework with similarity learning and consistency learning, where our
pooling module is adversarially trained with respect to the encoder for
adversarial robustness. Experiments on twelve datasets on both graph
classification and transfer learning tasks verify the superiority of the
proposed method over its counterparts.

自我监督图表示学习是最近在生物信息学和社交网络等领域显示出相当潜力的技术。本文介绍了一种名为 Graph Pooling ContraSt（GPS）的新方法，通过重新思考图池化（graph pooling）并利用其自动生成多尺度的正视图，从而在相似性学习和一致性学习的共同对比学习框架中将强增强视图和弱增强视图结合起来，并对编码器进行对抗训练以提高其对抗鲁棒性。实验结果验证了该方法在图分类和迁移学习任务中的优越性。