SW-VAE: Weakly Supervised Learn Disentangled Representation Via Latent Factor Swapping
This addresses the challenge of inadequate disentanglement in unsupervised learning for representation learning tasks, offering a weakly-supervised solution.
The authors tackled the problem of representation disentanglement by proposing SW-VAE, a weakly-supervised training approach that uses pairs of input observations as supervision signals, showing significant improvement over state-of-the-art methods on several datasets.
Representation disentanglement is an important goal of representation learning that benefits various downstream tasks. To achieve this goal, many unsupervised learning representation disentanglement approaches have been developed. However, the training process without utilizing any supervision signal have been proved to be inadequate for disentanglement representation learning. Therefore, we propose a novel weakly-supervised training approach, named as SW-VAE, which incorporates pairs of input observations as supervision signals by using the generative factors of datasets. Furthermore, we introduce strategies to gradually increase the learning difficulty during training to smooth the training process. As shown on several datasets, our model shows significant improvement over state-of-the-art (SOTA) methods on representation disentanglement tasks.