Learning Robust Representations via Multi-View Information Bottleneck (2002.07017v2)

Abstract: The information bottleneck principle provides an information-theoretic method for representation learning, by training an encoder to retain all information which is relevant for predicting the label while minimizing the amount of other, excess information in the representation. The original formulation, however, requires labeled data to identify the superfluous information. In this work, we extend this ability to the multi-view unsupervised setting, where two views of the same underlying entity are provided but the label is unknown. This enables us to identify superfluous information as that not shared by both views. A theoretical analysis leads to the definition of a new multi-view model that produces state-of-the-art results on the Sketchy dataset and label-limited versions of the MIR-Flickr dataset. We also extend our theory to the single-view setting by taking advantage of standard data augmentation techniques, empirically showing better generalization capabilities when compared to common unsupervised approaches for representation learning.

• The paper extends the traditional Information Bottleneck principle to unsupervised multi-view settings by designing a novel MIB loss that retains shared, task-relevant information.
• It incorporates single-view data augmentation to simulate multiple views, enhancing robustness without relying on labeled data.
• Empirical results on Sketchy and MIR-Flickr datasets demonstrate improved performance and generalization in low-label scenarios.

Essay on "Learning Robust Representations via Multi-View Information Bottleneck"

The paper "Learning Robust Representations via Multi-View Information Bottleneck" by Marco Federici et al. presents an innovative extension of the Information Bottleneck (IB) principle to multi-view unsupervised representation learning. The authors tackle the pervasive challenge of retaining task-relevant information while discarding superfluous data, without relying on labeled data.

Core Contributions

1. Extension of Information Bottleneck Principle: The paper extends the traditional IB principle, which operates under supervised settings, to multi-view unsupervised learning environments. In these settings, the encoder learns to retain information shared between different views, thereby preserving only the task-relevant components. This is achieved by defining a Multi-View Information Bottleneck (MIB) loss function. The loss function maximizes mutual information between representations from different views while minimizing the inclusion of redundant information.
2. Theoretical Analysis: A rigorous theoretical framework supports the proposed MIB model, underpinned by the notion of redundancy in multi-view learning. The theory stipulates that if two views are mutually redundant for a task, then a representation that is sufficient for one view can be sufficient for the task overall. This lays the groundwork for eliminating view-specific nuisances and improving the robustness of learned representations.
3. Single-View Augmentation: The paper ingeniously links the MIB framework to single-view settings by using data augmentation techniques. These transformations simulate multiple views of the same data, enabling the retention of invariant information without direct label supervision. This allows for generalization in the absence of traditional multi-view datasets.
4. Empirical Validation: The MIB model is empirically validated through state-of-the-art results on the Sketchy and MIR-Flickr datasets. These results demonstrate the model's efficacy, particularly in low-label scenarios, showing that it can surpass existing multi-view and unsupervised learning methods in terms of generalization capacity.

Numerical Results and Claims

The authors report significant advances in tasks such as sketch-based image retrieval and multi-view representation learning. On the Sketchy dataset, the MIB model achieves a notable mean average precision (mAP) and reports improvements over previous high-performing models. In experiments on the MIR-Flickr dataset, the model shows enhanced performance with sparse labeled datasets, highlighting its robustness and data efficiency.

Implications and Future Work

The implications of this research are manifold, touching on both theoretical and practical domains. Theoretically, the framework suggests new avenues for exploring redundancy among data views and its role in representation learning. Practically, the approach may influence the development of more efficient models in fields like computer vision and natural language processing, where labeled data is scarce or expensive to obtain.

Looking forward, the potential extensions of this work could include the exploration of more sophisticated data augmentation techniques and the application of the MIB framework to other unsupervised domains or semi-supervised learning paradigms. Further, investigating the limits of redundancy-based representation learning across more diverse datasets could yield deeper insights into the underlying mechanisms powering robust representation learning.

In conclusion, "Learning Robust Representations via Multi-View Information Bottleneck" presents a comprehensive and technically rigorous exploration of unsupervised representation learning. It leverages the synergies between mutual information principles and multi-view learning to push the boundaries of what can be achieved without direct supervision, promising significant advances in both theory and application.