Inductive biases are crucial in disentangled representation learning for narrowing down an underspecified solution set. In this work, we consider endowing a neural network autoencoder with three select inductive biases from the literature: data compression into a grid-like latent space via quantization, collective independence amongst latents, and minimal functional influence of any latent on how other latents determine data generation. In principle, these inductive biases are deeply complementary: they most directly specify properties of the latent space, encoder, and decoder, respectively. In practice, however, naively combining existing techniques instantiating these inductive biases fails to yield significant benefits. To address this, we propose adaptations to the three techniques that simplify the learning problem, equip key regularization terms with stabilizing invariances, and quash degenerate incentives. The resulting model, Tripod, achieves state-of-the-art results on a suite of four image disentanglement benchmarks. We also verify that Tripod significantly improves upon its naive incarnation and that all three of its "legs" are necessary for best performance.

在这项研究中，我们通过将神经网络自编码器与三种从文献中选取的归纳偏差相结合，即通过量化将数据压缩成网格状潜在空间、潜在变量之间的集体独立性和最小功能对其他潜在变量数据生成的功能影响减至最小，提出了一种新的模型Tripod，该模型在四个图像分离基准测试中取得了最先进的结果，实验证明Tripod明显优于其朴素形式，并且其三个“腿”对于最佳性能是必要的。

三脚架：用于解缠合表示学习的三个互补归纳偏差