你不需要强假设：通过时间差分的视觉表示学习

摘要

假设更少的方法在很大程度上推动了人工智能的进步。随着计算能力和数据的增加，归纳偏置较弱的方法通常优于假设更强的方法。这在视觉表示学习领域尤为典型——该领域的方法已从监督学习主导，经弱监督学习，发展至如今无需人工标注的自监督学习的广泛成功。然而，即便是现代自监督学习方法，仍依赖于数据增强、掩码或裁剪等强归纳偏置。若这一趋势持续，即便是这些残留的偏置也会在大规模场景下成为瓶颈——我们的实验证实了这一点：随着数据增长，归纳偏置的最优强度会逐渐降低。这促使我们探索依赖更少假设的方法。为此，我们提出视觉时序差分（Temporal Difference in Vision，TDV），一种从视频中自监督学习的新范式，该方法规避了现有归纳偏置，转而依赖一个因果假设：过去引发未来。TDV通过联合训练图像编码器和运动编码器，使得当前帧的表示加上编码后的运动等于下一帧的表示。尽管未利用任何强归纳偏置，TDV在密集空间任务上仍能与最先进方法媲美，为无强假设的表示学习奠定了基础。

English

Progress in AI has largely been driven by methods that assume less. As compute and data increase, approaches with weaker inductive biases generally outperform those with stronger assumptions. This is particularly characteristic of the field of Visual Representation Learning, where approaches have gone from being dominated by Supervised Learning, to Weakly Supervised Learning, to the now widespread success of Self-Supervised Learning without human labels. Yet, even modern Self-Supervised Learning approaches still depend on strong inductive biases such as augmentations, masking, or cropping. If this trend holds, even these remaining biases should become bottlenecks at scale -- and our experiments confirm this: the optimal strength of inductive biases decreases as data grows. This motivates the search for approaches that rely on fewer assumptions. To this end, we introduce Temporal Difference in Vision (TDV), a new paradigm for self-supervised learning from video that avoids existing inductive biases, relying instead on a causal assumption that the past causes the future. TDV functions by jointly training an image encoder and a motion encoder so that the current frame's representation plus the encoded motion equals the next frame's representation. Despite not leveraging any strong inductive biases, TDV matches state-of-the-art recipes on dense spatial tasks, laying the foundation for representation learning without strong assumptions.