CSG: Curriculum Representation Learning for Signed Graph

Signed graphs are valuable for modeling complex relationships with positive and negative connections, and Signed Graph Neural Networks (SGNNs) have become crucial tools for their analysis. However, prior to our work, no specific training plan existed for SGNNs, and the conventional random sampling approach did not address varying learning difficulties within the graph's structure. We proposed a curriculum-based training approach, where samples progress from easy to complex, inspired by human learning. To measure learning difficulty, we introduced a lightweight mechanism and created the Curriculum representation learning framework for Signed Graphs (CSG). This framework optimizes the order in which samples are presented to the SGNN model. Empirical validation across six real-world datasets showed impressive results, enhancing SGNN model accuracy by up to 23.7% in link sign prediction (AUC) and significantly improving stability with an up to 8.4 reduction in the standard deviation of AUC scores.

DRVC: A Framework of Any-to-Any Voice Conversion with Self-Supervised Learning

Any-to-any voice conversion problem aims to convert voices for source and target speakers, which are out of the training data. Previous works wildly utilize the disentangle-based models. The disentangle-based model assumes the speech consists of content and speaker style information and aims to untangle them to change the style information for conversion. Previous works focus on reducing the dimension of speech to get the content information. But the size is hard to determine to lead to the untangle overlapping problem. We propose the Disentangled Representation Voice Conversion (DRVC) model to address the issue. DRVC model is an end-to-end self-supervised model consisting of the content encoder, timbre encoder, and generator. Instead of the previous work for reducing speech size to get content, we propose a cycle for restricting the disentanglement by the Cycle Reconstruct Loss and Same Loss. The experiments show there is an improvement for converted speech on quality and voice similarity.