TNF: Tri-branch Neural Fusion for Multimodal Medical Data Classification

This paper presents a Tri-branch Neural Fusion (TNF) approach designed for classifying multimodal medical images and tabular data. It also introduces two solutions to address the challenge of label inconsistency in multimodal classification. Traditional methods in multi-modality medical data classification often rely on single-label approaches, typically merging features from two distinct input modalities. This becomes problematic when features are mutually exclusive or labels differ across modalities, leading to reduced accuracy. To overcome this, our TNF approach implements a tri-branch framework that manages three separate outputs: one for image modality, another for tabular modality, and a third hybrid output that fuses both image and tabular data. The final decision is made through an ensemble method that integrates likelihoods from all three branches. We validate the effectiveness of TNF through extensive experiments, which illustrate its superiority over traditional fusion and ensemble methods in various convolutional neural networks and transformer-based architectures across multiple datasets.

WeaveNet for Approximating Two-sided Matching Problems

Matching, a task to optimally assign limited resources under constraints, is a fundamental technology for society. The task potentially has various objectives, conditions, and constraints; however, the efficient neural network architecture for matching is underexplored. This paper proposes a novel graph neural network (GNN), \textit{WeaveNet}, designed for bipartite graphs. Since a bipartite graph is generally dense, general GNN architectures lose node-wise information by over-smoothing when deeply stacked. Such a phenomenon is undesirable for solving matching problems. WeaveNet avoids it by preserving edge-wise information while passing messages densely to reach a better solution. To evaluate the model, we approximated one of the \textit{strongly NP-hard} problems, \textit{fair stable matching}. Despite its inherent difficulties and the network's general purpose design, our model reached a comparative performance with state-of-the-art algorithms specially designed for stable matching for small numbers of agents.

Edge-Selective Feature Weaving for Point Cloud Matching

This paper tackles the problem of accurately matching the points of two 3D point clouds. Most conventional methods improve their performance by extracting representative features from each point via deep-learning-based algorithms. On the other hand, the correspondence calculation between the extracted features has not been examined in depth, and non-trainable algorithms (e.g. the Sinkhorn algorithm) are frequently applied. As a result, the extracted features may be forcibly fitted to a non-trainable algorithm. Furthermore, the extracted features frequently contain stochastically unavoidable errors, which degrades the matching accuracy. In this paper, instead of using a non-trainable algorithm, we propose a differentiable matching network that can be jointly optimized with the feature extraction procedure. Our network first constructs graphs with edges connecting the points of each point cloud and then extracts discriminative edge features by using two main components: a shared set-encoder and an edge-selective cross-concatenation. These components enable us to symmetrically consider two point clouds and to extract discriminative edge features, respectively. By using the extracted discriminative edge features, our network can accurately calculate the correspondence between points. Our experimental results show that the proposed network can significantly improve the performance of point cloud matching. Our code is available at https://github.com/yanarin/ESFW

Adaptive Distillation for Decentralized Learning from Heterogeneous Clients

This paper addresses the problem of decentralized learning to achieve a high-performance global model by asking a group of clients to share local models pre-trained with their own data resources. We are particularly interested in a specific case where both the client model architectures and data distributions are diverse, which makes it nontrivial to adopt conventional approaches such as Federated Learning and network co-distillation. To this end, we propose a new decentralized learning method called Decentralized Learning via Adaptive Distillation (DLAD). Given a collection of client models and a large number of unlabeled distillation samples, the proposed DLAD 1) aggregates the outputs of the client models while adaptively emphasizing those with higher confidence in given distillation samples and 2) trains the global model to imitate the aggregated outputs. Our extensive experimental evaluation on multiple public datasets (MNIST, CIFAR-10, and CINIC-10) demonstrates the effectiveness of the proposed method.