RepresentationLearning

Inourexperiments, disentanglement models andemergentlanguage (EL) models use the same architectures for the convolutional encoder and decoder. On the dSprites dataset, we use 500 samples to train linear or GBT readout models for classification and regressions tasks. Their generalization performance is given in Table 3. The attribute values are expected to generalize perfectly with linear orGBT readout models. Compositional latent variables may not be the best representations for downstream tasks.