A number of recent self-supervised learning methods have shown impressive performance on image classification and other tasks. A somewhat bewildering variety of techniques have been used, not always with a clear understanding of the reasons for their benefits, especially when used in combination. Here we treat the embeddings of images as point particles and consider model optimization as a dynamic process on this system of particles. Our dynamic model combines an attractive force for similar images, a locally dispersive force to avoid local collapse, and a global dispersive force to achieve a globally-homogeneous distribution of particles. The dynamic perspective highlights the advantage of using a delayed-parameter image embedding (a la BYOL) together with multiple views of the same image. It also uses a purely-dynamic local dispersive force (Brownian motion) that shows improved performance over other methods and does not require knowledge of other particle coordinates. The method is called MSBReg which stands for (i) a Multiview centroid loss, which applies an attractive force to pull different image view embeddings toward their centroid, (ii) a Singular value loss, which pushes the particle system toward spatially homogeneous density, (iii) a Brownian diffusive loss. We evaluate downstream classification performance of MSBReg on ImageNet as well as transfer learning tasks including fine-grained classification, multi-class object classification, object detection, and instance segmentation. In addition, we also show that applying our regularization term to other methods further improves their performance and stabilize the training by preventing a mode collapse.
翻译:最近一些自我监督的学习方法在图像分类和其他任务方面表现出令人印象深刻的成绩。 已经使用了一些有些令人困惑的多种技术, 并不总是能够清楚地理解其好处的原因, 特别是在结合使用时。 我们在这里将图像嵌入作为点粒子, 并将模型优化视为这个粒子系统的动态过程。 我们的动态模型结合了对类似图像的吸引力、 当地分散力以避免本地崩溃, 以及全球分散力量, 以实现全球粒子均匀分布。 动态视角凸显了使用延迟参数图像嵌入( la BYOL) 以及同一图像的多重观点的好处。 我们还使用一种纯动态的本地分散力( 布朗尼运动), 显示比其他粒子系统更好的性能, 不需要了解其他粒子坐标。 这个方法叫做 MSBRIeg, 代表( 一) 多视图百分解损失, 运用一种吸引力将不同图像嵌入到其正固值, (二) Singalalalal- 目标损失, 将我们进行磁性分析的精度分析, 将磁力分析系统 显示一个稳定的磁质性分类,, 学习磁性分析。