In this work, we propose to tackle the problem of domain generalization in the context of textit{insufficient samples}. Instead of extracting latent feature embeddings based on deterministic models, we propose to learn a domain-invariant representation based on the probabilistic framework by mapping each data point into probabilistic embeddings. Specifically, we first extend empirical maximum mean discrepancy (MMD) to a novel probabilistic MMD that can measure the discrepancy between mixture distributions (i.e., source domains) consisting of a series of latent distributions rather than latent points. Moreover, instead of imposing the contrastive semantic alignment (CSA) loss based on pairs of latent points, a novel probabilistic CSA loss encourages positive probabilistic embedding pairs to be closer while pulling other negative ones apart. Benefiting from the learned representation captured by probabilistic models, our proposed method can marriage the measurement on the textit{distribution over distributions} (i.e., the global perspective alignment) and the distribution-based contrastive semantic alignment (i.e., the local perspective alignment). Extensive experimental results on three challenging medical datasets show the effectiveness of our proposed method in the context of insufficient data compared with state-of-the-art methods.
This article addresses the problem of domain generalization in the context of insufficient samples. The authors propose a novel approach that utilizes probabilistic embeddings to learn a domain-invariant representation. They introduce a probabilistic maximum mean discrepancy (MMD) to measure the discrepancy between mixture distributions, and a probabilistic contrastive semantic alignment (CSA) loss to encourage positive probabilistic embedding pairs to be closer while pulling negative ones apart. By leveraging probabilistic models, their method combines global perspective alignment and local perspective alignment to capture the distribution over distributions. The effectiveness of their approach is demonstrated through extensive experiments on three challenging medical datasets, highlighting its superiority in dealing with insufficient data compared to existing methods.
In this article, we will explore a novel approach to the problem of domain generalization in the context of insufficient samples. Traditional methods for this problem often rely on deterministic models to extract latent feature embeddings. However, we propose a new solution that utilizes the power of probabilistic frameworks, allowing us to learn a domain-invariant representation by mapping each data point into probabilistic embeddings.
Probabilistic Maximum Mean Discrepancy
To measure the discrepancy between mixture distributions (i.e., source domains) consisting of a series of latent distributions, we introduce a novel concept called probabilistic Maximum Mean Discrepancy (MMD). This extension of the empirical MMD provides a more accurate measurement by considering the entire distribution rather than individual latent points. By capturing the uncertainty and diversity within each distribution, we are able to better understand the differences between domains.
Probabilistic Contrastive Semantic Alignment
A key aspect of our proposed method is the Contrastive Semantic Alignment (CSA) loss, which encourages positive embedding pairs to be closer while pushing negative pairs apart. In traditional approaches, this loss is imposed based on pairs of latent points. However, we present a new Probabilistic CSA loss that operates on probabilistic embeddings. By considering the entire distribution rather than single points, we can better account for uncertainty and variations within each domain.
Marriage of Global and Local Alignment
Our proposed method benefits from the learned representation captured by probabilistic models. It combines the measurement on the distribution over distributions (global perspective alignment) with the distribution-based Contrastive Semantic Alignment (local perspective alignment). This marriage of global and local alignment allows us to capture both macro-level and micro-level differences between domains, providing a comprehensive understanding of the data.
Experimental Results
To evaluate the effectiveness of our proposed method, we conducted extensive experiments on three challenging medical datasets. These datasets are known for having insufficient data, making them the perfect testing ground for our approach. Our method outperformed state-of-the-art methods, demonstrating its ability to effectively generalize domains with limited samples.
In conclusion, we have presented a novel approach to the problem of domain generalization in the context of insufficient samples. By utilizing probabilistic frameworks and considering the entire distribution of data points, we are able to learn a domain-invariant representation that captures both global and local alignment. Our experimental results show the superiority of our method compared to existing approaches. This research opens up new possibilities for tackling the challenges of domain generalization and insufficient data in various domains.
The proposed work addresses the problem of domain generalization in the context of insufficient samples. This is a crucial problem in machine learning, as models trained on one domain often fail to generalize well to other domains, especially when there is a lack of labeled data.
The authors propose a novel approach that focuses on learning a domain-invariant representation using a probabilistic framework. Instead of relying on deterministic models to extract latent feature embeddings, they map each data point into probabilistic embeddings. This allows them to capture the uncertainty and variability in the data, which is particularly useful when dealing with limited samples.
One key contribution of this work is the extension of empirical maximum mean discrepancy (MMD) to a probabilistic MMD. The authors propose a novel probabilistic MMD that can measure the discrepancy between mixture distributions, which are composed of a series of latent distributions. This is a significant improvement over existing methods that only consider individual latent points.
Another important aspect of the proposed method is the contrastive semantic alignment (CSA) loss. Instead of imposing this loss on pairs of latent points, the authors introduce a probabilistic CSA loss. This loss encourages positive probabilistic embedding pairs to be closer while pushing apart negative ones. By incorporating this probabilistic CSA loss, the authors are able to capture the semantic relationships between data points in a more robust and expressive manner.
The combination of the probabilistic MMD and probabilistic CSA losses allows the proposed method to effectively align both the global and local perspectives of the data. The measurement on the distribution over distributions enables the model to capture the overall structure and variability across different domains, while the distribution-based contrastive semantic alignment ensures that similar data points are grouped together and dissimilar ones are separated.
The experimental results on three challenging medical datasets demonstrate the effectiveness of the proposed method in the context of insufficient data. The proposed method outperforms state-of-the-art approaches, highlighting its ability to generalize well even with limited samples. This is a significant contribution to the field, as it addresses a major limitation in domain generalization and has potential applications in various domains where labeled data is scarce.
In conclusion, the proposed method provides a novel approach to tackle the problem of domain generalization in the context of insufficient samples. By leveraging probabilistic embeddings and introducing probabilistic MMD and CSA losses, the method effectively learns a domain-invariant representation that captures the global and local perspectives of the data. The experimental results demonstrate its superiority over existing methods, making it a promising solution for real-world applications with limited labeled data.
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