有效的蒙面自动编码器具有自我一致性
概括
具有自我一致性的高效掩盖自编码器 (EMAE) 通过提高预训练效率和预测一致性来改善掩盖图像建模 (MIM). 这种新的方法导致更可靠的图像表示和更快的训练时间.
科学领域:
- 计算机视觉 计算机视觉
- 自主监督学习学习
背景情况:
- 蒙面图像建模 (MIM) 是一种强大的自主监督的计算机视觉预训练技术,灵感来自蒙面语言建模 (MLM).
- 在MIM中高随机面具比率导致数据利用效率低下和不一致的预测,阻碍了训练前的速度和可靠性.
研究的目的:
- 引入具有自我一致性的高效面具自动编码器 (EMAE),以提高MIM预培训效率和预测一致性.
- 解决长时间的预训练和不可靠的代人的局限性,这是由于传统MIM中的高面罩比率造成的.
主要方法:
- 一种并行掩盖策略将图像划分为K个部分,每个部分都被掩盖并并行处理在一个代中.
- 自我一致性学习用于确保在不同部分重叠的掩盖补丁的一致预测.
- 该模型在并行处理过程中最大限度地减少了预测和掩盖补丁之间的损失.
主要成果:
- EMAE显著提高了预训练效率,在ViT-Large上实现了最高性能,MAE在ImageNet上的预训练时间仅为13%.
- 该方法证明了优异的数据利用率,并产生了更可靠的图像表示.
- EMAE在各种下游任务中实现了最先进的可转移性,包括图像分类,对象检测和语义细分.
结论:
- EMAE提供了一种更有效和更一致的方法,用于掩盖图像建模预培训.
- 拟议的并行面具策略和自我一致性学习有效地克服了传统MIM的局限性.
- EMAE为各种应用中高性能计算机视觉模型提供了坚实的基础.
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