Improving Disentangled Representation Learning with Synthetic Data

Learning to represent data in a clear and structured way is important. When we talk about representation learning, we refer to methods that help break down complex data into simpler parts. One idea in this area is called Disentangled Representation Learning (DRL). This approach aims to separate different factors in the data so we can understand and work with them better. However, using DRL with real images has not been fully successful. This is often due to different factors in the data being linked to each other, the quality of the images, and the difficulty of getting accurate labels for the data.

In this discussion, we will focus on how to use synthetic data to improve DRL for real-world images. We will look at how Fine-tuning affects the learning process and which characteristics of the learned representations can be transferred successfully. We will present various tests and metrics that help us evaluate the effectiveness of this approach.

#The Importance of Good Representation

Creating clear and useful representations is a key part of learning from data. DRL aims to build models that identify and separate the different underlying factors that affect the data. This means we want to capture these factors in a way that is easy to interpret, independent of specific tasks. The benefits of using DRL include better clarity, stability, and the ability to apply the results to a variety of situations.

Disentangled representations have shown to be useful for various tasks. These include predicting factors, creating and translating images, ensuring fairness in classification, reasoning abstractly, adapting to new domains, and handling data that is outside of the usual range. While many methods use different definitions of disentanglement, they generally agree that having some guidance on the factors is helpful.

However, labeling each factor can be costly and sometimes impossible. Therefore, DRL has often been tested using synthetic or simulated data, which is easier to control but may not reflect real-world challenges like clutter, occlusion, and the correlation between factors.

#Addressing Real-World Challenges with DRL Transfer

In this work, we suggest using a method for transferring disentangled representations learned from synthetic data to real data. The idea is to use a weakly supervised approach. This means that we will learn on datasets where the factors are known and labeled, and then apply that knowledge to target datasets where the factors are not available or easy to identify.

Our goal is to treat real datasets as targets while using synthetic data as the source. We provide three main contributions:

1. A new metric to evaluate the quality of disentanglement, which is easy to understand and classifier-free.
2. A method for transferring disentangled representations to target datasets without needing factor annotations.
3. A detailed empirical analysis examining different source and target pairs.

Next, we will explore how we assess the quality of disentanglement, looking at existing metrics and their limitations.

#Evaluating Disentanglement Quality

There is no single definition for disentanglement, but there is a consensus on what properties a good representation should have. We categorize the existing metrics into three main groups:

1. Intervention-based Metrics: These compare codes through controlled changes to the data. They create groups where certain factors are kept constant or where only one factor changes. Examples include BetaVAE and FactorVAE.

2. Predictor-based Metrics: These use classifiers or regressors to predict factors from the disentangled representation. Metrics like DCI Disentanglement and SAP fall into this category.

3. Information-based Metrics: These rely on principles from information theory to assess the relationships between factors and representations.

Among these metrics, the intervention-based ones allow for better control but depend heavily on classifiers. Thus, their results can vary based on classifier choices and settings. Information-based methods focus on mutual information, which requires careful estimation.

To address these limitations, we introduce a new metric called OMES (Overlap Multiple Encoding Scores). This metric evaluates the quality of factor encoding while providing insight into the structure of representation. OMES measures two main qualities: modularity (how factors overlap) and compactness (how well a factor is encoded across representation dimensions).

#OMES Metric Explained

OMES analyzes the overlap of factors in the representation and penalizes factors that share dimensions. By examining images that differ only in one factor, we can establish a correlation between representation dimensions and the factors. The metric provides an overall score and individual scores for each factor, allowing us to see how different settings affect disentanglement.

We found that OMES aligns well with existing metrics. It shows a strong correlation with other well-known metrics like MIG and DCI while being more descriptive. This makes it a powerful tool for assessing the quality of disentangled representations.

#Transferring Disentangled Representations

Fully unsupervised disentangled representation learning often struggles in real scenarios. Annotating all factors can be critical but can also present challenges. Our goal is to develop a way to transfer disentangled representations from synthetic datasets-where the factors are known-to unsupervised real datasets.

We explore various Transfer Learning scenarios, looking at pairs of source and target datasets to evaluate how well disentanglement transfers. We employ methods like weakly supervised learning to create strong representations on the source and then apply them to the target.

Our main research questions include:

1. How effectively can the disentangled representation transfer, and does it depend on how closely related the source and target datasets are?
2. Which aspects of the representation remain intact after the transfer?
3. Does fine-tuning improve the quality of the disentangled representation on the target dataset?

In our experiments, we use both synthetic and real datasets, aiming to cover a wide range of challenges.

#Experimental Analysis

#Datasets Used

To carry out our analysis, we relied on several datasets with various characteristics. Some datasets are DRL-compliant, which means the factors are independent and fully known. Others, like dSprites and its variants, include known factors such as shape, scale, rotation, and positions.

For real datasets, we look at collections that present real-world challenges, including variations in background and the presence of hidden factors. By using these datasets, we aim to reflect real-world complexity while testing our framework's capabilities.

#Training Process

For the experiments, we trained multiple models on the synthetic datasets, using a consistent training strategy. We employed gradient boosted trees and multilayer perceptrons for classification tasks. These classifiers help us evaluate how well the representations work on the target datasets.

Fine-tuning was performed on the target data, allowing the models to adapt and improve their performance in real-world tasks.

#Evaluation Metrics

For evaluating representation quality, we employed various metrics, including OMES, DCI, MIG, and others that assess modularity and compactness. By analyzing the classification accuracy for different factors, we can determine how well the representation captures the underlying structure of the data.

#Results of the Analysis

#Synthetic to Synthetic Transfer

When transferring representations between synthetic datasets, we found that when both source and target datasets have the same factors, the performance remains stable. Fine-tuning generally leads to improved results, particularly regarding how well we can interpret the representation.

However, when we introduced a new factor in the target dataset, while the original factors were classified well, the new factor showed lower accuracy initially. Fine-tuning helped improve performance, particularly when considering the entire representation.

#Synthetic to Real Transfer

When moving from synthetic to real datasets, we observed that the ability to transfer representations depends heavily on the similarity between the source and target. Factors that are closely related are more likely to be well represented. Fine-tuning proved beneficial, particularly in maintaining the clarity of the representation.

For instance, when using synthetic data with known factors to improve classification accuracy on a real target dataset, we noted that factors less represented in the synthetic data struggled more when applied to the real data. Fine-tuning was crucial to bridging this gap.

#Real to Real Transfer

Transferring from one real dataset to another real dataset also revealed some challenges. When using a simplified version of a target dataset as the source, we expected improved performance. However, the results did not meet expectations, indicating that simply simplifying the data did not enhance the representation quality.

Experiments showed that transferring from one real dataset to another, where both had different characteristics, resulted in mixed outcomes. Some factors transferred better than others, with performance varying based on the complexity of the data.

#Conclusion

This work highlights the potential of transferring disentangled representations learned from synthetic datasets to real datasets, which can often lack labeled factors. Our approach focuses on weakly supervised learning to create strong representations that can adapt to the complexity of real-world data.

Through experimental analysis, we found that while some properties of disentangled representations are preserved during transfer, others may degrade, particularly when moving from synthetic to real environments. Fine-tuning plays a major role in improving performance and is generally necessary for maintaining clarity and organization in representation.

The OMES metric we introduced provides a valuable tool for measuring the quality of disentangled representations and allows us to evaluate transfer success. Future work will focus on testing our methods on more complex real datasets and exploring more specific applications in fields such as biomedical imaging and action recognition.