Vector Embeddings with Subvector Permutation Invariance using a Triplet Enhanced Autoencoder

基于三元组增强卷积自编码器的MNIST无监督深度聚类

Unsupervised Deep Clustering of MNIST with Triplet-Enhanced Convolutional Autoencoders

本研究提出基于两阶段三元组增强卷积自编码器的MNIST无监督深度聚类方法，结合重构误差与KMeans聚类损失，辅以批量归一化等技术，在多项聚类指标上取得优异性能，兼顾精度与可扩展性。

Novelty Detection and Analysis of Traffic Scenario Infrastructures in the Latent Space of a Vision Transformer-Based Triplet Autoencoder

Detecting unknown and untested scenarios is crucial for scenario-based testing. Scenario-based testing is considered to be a possible approach to validate autonomous vehicles. A traffic scenario consists of multiple components, with infrastructure being one of it. In this work, a method to detect novel traffic scenarios based on their infrastructure images is presented. An autoencoder triplet network provides latent representations for infrastructure images which are used for outlier detection. The triplet training of the network is based on the connectivity graphs of the infrastructure. By us…

TVAE: Triplet-Based Variational Autoencoder using Metric Learning

Deep metric learning has been demonstrated to be highly effective in learning semantic representation and encoding information that can be used to measure data similarity, by relying on the embedding learned from metric learning. At the same time, variational autoencoder (VAE) has widely been used to approximate inference and proved to have a good performance for directed probabilistic models. However, for traditional VAE, the data label or feature information are intractable. Similarly, traditional representation learning approaches fail to represent many salient aspects of the data. In this …

Incremental Class Learning using Variational Autoencoders with Similarity Learning

Catastrophic forgetting in neural networks during incremental learning remains a challenging problem. Previous research investigated catastrophic forgetting in fully connected networks, with some earlier work exploring activation functions and learning algorithms. Applications of neural networks have been extended to include similarity learning. Understanding how similarity learning loss functions would be affected by catastrophic forgetting is of significant interest. Our research investigates catastrophic forgetting for four well-known similarity-based loss functions during incremental class…

Drug-Drug Interaction Prediction with Wasserstein Adversarial Autoencoder-based Knowledge Graph Embeddings

Interaction between pharmacological agents can trigger unexpected adverse events. Capturing richer and more comprehensive information about drug-drug interactions (DDI) is one of the key tasks in public health and drug development. Recently, several knowledge graph embedding approaches have received increasing attention in the DDI domain due to their capability of projecting drugs and interactions into a low-dimensional feature space for predicting links and classifying triplets. However, existing methods only apply a uniformly random mode to construct negative samples. As a consequence, these…

基于端到端三元组损失的网络嵌入微调以实现有效的PII检测

End-to-End triplet loss based fine-tuning for network embedding in effective PII detection

针对移动数据生态系统中PII泄露检测需外部特征选择的问题，提出基于预训练LLM、自动编码器及三元组损失微调的端到端框架，利用真实数据集提升检测效果并与现有方法对比。