Weakly-Supervised Disentanglement Without Compromises (2002.02886v4)

Abstract: Intelligent agents should be able to learn useful representations by observing changes in their environment. We model such observations as pairs of non-i.i.d. images sharing at least one of the underlying factors of variation. First, we theoretically show that only knowing how many factors have changed, but not which ones, is sufficient to learn disentangled representations. Second, we provide practical algorithms that learn disentangled representations from pairs of images without requiring annotation of groups, individual factors, or the number of factors that have changed. Third, we perform a large-scale empirical study and show that such pairs of observations are sufficient to reliably learn disentangled representations on several benchmark data sets. Finally, we evaluate our learned representations and find that they are simultaneously useful on a diverse suite of tasks, including generalization under covariate shifts, fairness, and abstract reasoning. Overall, our results demonstrate that weak supervision enables learning of useful disentangled representations in realistic scenarios.

• The paper establishes that paired non-i.i.d. observations can identify latent factors without heavy supervision.
• It introduces practical algorithms that use image pairs without explicit annotations, validated through a large-scale empirical study.
• The learned representations outperform unsupervised methods in generalization, fairness, and abstract reasoning tasks.

An Expert Overview of "Weakly-Supervised Disentanglement Without Compromises"

The paper "Weakly-Supervised Disentanglement Without Compromises" by Locatello et al. discusses novel approaches to disentangled representation learning under weak supervision. It tackles the theoretical and practical challenges posed by the unsupervised disentanglement task, which has been proven non-identifiable without strong inductive biases. The authors present a framework allowing the identifiability of latent factors using paired non-i.i.d. observations, significantly reducing the supervision requirements compared to previous methods.

Core Contributions

The paper makes several significant contributions to the field of disentangled representation learning:

1. Theoretical Establishment of Disentanglement from Pairs: The authors begin with a rigorous theoretical analysis, demonstrating that weak supervision in the form of pairs of observations, differing by a limited number of factors, is sufficient for learning disentangled representations. This finding challenges the established notion that unsupervised learning from i.i.d. observations is intrinsically non-identifiable.
2. Practical Algorithms for Weakly-Supervised Learning: The authors introduce efficient practical algorithms that leverage image pairs without requiring explicit annotations of the groups or changes in factors. These algorithms provide flexibility and applicability in broader settings where such weakly-supervised data is naturally available.
3. Large-Scale Empirical Study: An extensive empirical evaluation is performed, involving over 15,000 trained models on various benchmark datasets. The results consistently show that weak supervision can reliably yield disentangled representations, outperforming unsupervised methods without needing explicit annotations for model selection.
4. Downstream Task Evaluation: The learned representations are evaluated across various tasks, including those measuring generalization under covariate shifts, fairness, and abstract reasoning. These evaluations show that weakly-supervised models often surpass purely unsupervised approaches, underscoring the practical utility of the disentangled representations obtained under the proposed framework.

Implications

Theoretical Implications

The theoretical findings have substantial implications for disentanglement research. By establishing identifiability from pairs of observations under weak supervision, this work suggests a shift towards data setups that can naturally provide weak supervision signals. The relaxation of assumptions contributes to the understanding of what is possible within the latent variable models used for representation learning.

Practical Implications

From a practical standpoint, this paper opens up new avenues for applying disentangled representation learning to real-world data. This approach is applicable in various scenarios where observations can be paired with minimal control or additional labelling, such as in video data of robotic tasks. It advances the field by presenting methods that are both theoretically justified and empirically validated on diverse datasets, emphasizing the methods' reliability and generalizability.

Future Directions

While the focus on weak supervision is pioneering, there is potential to explore the integration of these methods in more complex and dynamic environments, possibly incorporating real-time feedback through reinforcement learning frameworks. Furthermore, blending the adaptive inference techniques introduced with partial domain knowledge could enhance learning where some latent structure information is available but incomplete.

Conclusion

The work by Locatello et al. represents a notable advancement in the development of weakly-supervised algorithms for disentanglement learning. By addressing the core challenge of unsupervised disentanglement's non-identifiability, this research not only provides theoretical solutions but also empirically validated algorithms that extend the applicability of disentangled representations in machine learning. This paper sets a precedent for future explorations into weakly-supervised and semi-supervised learning domains, promising exciting developments in representational learning techniques.