Kenji Suzuki

Lin Fan, Yafei Ou, Cenyang Zheng et al.

Multi-modal learning has shown exceptional performance in various tasks, especially in medical applications, where it integrates diverse medical information for comprehensive diagnostic evidence. However, there still are several challenges in multi-modal learning, 1. Heterogeneity between modalities, 2. uncertainty in missing modalities, 3. influence of intrinsic noise, and 4. interpretability for fusion result. This paper introduces the Modal-Domain Attention (MDA) model to address the above challenges. MDA constructs linear relationships between modalities through continuous attention, due to its ability to adaptively allocate dynamic attention to different modalities, MDA can reduce attention to low-correlation data, missing modalities, or modalities with inherent noise, thereby maintaining SOTA performance across various tasks on multiple public datasets. Furthermore, our observations on the contribution of different modalities indicate that MDA aligns with established clinical diagnostic imaging gold standards and holds promise as a reference for pathologies where these standards are not yet clearly defined. The code and dataset will be available.

Lingfeng Wang, Shisen Wang, Jin Qi et al.

Affective Behavior Analysis is an important part in human-computer interaction. Existing multi-task affective behavior recognition methods suffer from the problem of incomplete labeled datasets. To tackle this problem, this paper presents a semi-supervised model with a mean teacher framework to leverage additional unlabeled data. To be specific, a multi-task model is proposed to learn three different kinds of facial affective representations simultaneously. After that, the proposed model is assigned to be student and teacher networks. When training with unlabeled data, the teacher network is employed to predict pseudo labels for student network training, which allows it to learn from unlabeled data. Experimental results showed that our proposed method achieved much better performance than baseline model and ranked 4th in both competition track 1 and track 2, and 6th in track 3, which verifies that the proposed network can effectively learn from incomplete datasets.

Kenji Suzuki, Yoshiyuki Kobayashi, Takuya Narihira

Identifying the influence of training data for data cleansing can improve the accuracy of deep learning. An approach with stochastic gradient descent (SGD) called SGD-influence to calculate the influence scores was proposed, but, the calculation costs are expensive. It is necessary to temporally store the parameters of the model during training phase for inference phase to calculate influence sores. In close connection with the previous method, we propose a method to reduce cache files to store the parameters in training phase for calculating inference score. We only adopt the final parameters in last epoch for influence functions calculation. In our experiments on classification, the cache size of training using MNIST dataset with our approach is 1.236 MB. On the other hand, the previous method used cache size of 1.932 GB in last epoch. It means that cache size has been reduced to 1/1,563. We also observed the accuracy improvement by data cleansing with removal of negatively influential data using our approach as well as the previous method. Moreover, our simple and general proposed method to calculate influence scores is available on our auto ML tool without programing, Neural Network Console. The source code is also available.