Deep clustering models are trained based on self-supervision and pseudo-supervision. However, applying these techniques can cause Feature Randomness and Feature Drift. On one hand, Feature Randomness takes place when a considerable portion of the pseudo-labels do not match the true ones. On the other hand, Feature Drift takes place when there is a strong con-flict between the self-supervision and pseudo-supervision tasks. We propose ADEC (Adversarial Deep Embedded Clustering) a novel autoencoder-based clustering model, which relies on a discriminator network to reduce random features while avoiding the drifting effect. Experimental results validate that our model alleviates these problems and outperforms existing methods.

Recent graph clustering methods have resorted to Graph Auto-Encoders (GAEs). However, two important issues have been overlooked. First, the accumulative error, inflicted by learning from noisy clustering assignments, degrades the model’s effectiveness. This problem is called Feature Randomness (FR). Second, reconstructing the adjacency matrix sets the model to learn irrelevant similarities for the clustering task. This problem is called Feature Drift (FD). To address these issues, we first propose a sampling operator that triggers a protection mechanism against FR. second, we propose an operator Υ that triggers a correction mechanism against FD by transforming the reconstructed graph. Experimental results validate that our operators alleviate these problems and bring significant clustering improvement.

Variational Graph Auto-Encoders (VGAEs) have achieved promising performance in several applications. Some recent models incorporate the clustering inductive bias by imposing non-Gaussian prior distributions. However, the regularization term is practically insufficient to learn the clustering structures due to the mismatch between the target and the learned distributions. Thus, we formulate a new variational lower bound that incorporates an explicit clustering objective function. The introduction of a clustering objective leads to two problems. First, the latent information destroyed by the clustering process is critical for generating the between-cluster edges. Second, the noisy and sparse input graph does not benefit from the information learned during the clustering process. To address the first problem, we identify a new term overlooked by existing Evidence Lower BOunds (ELBOs). This term accounts for the difference between the variational posterior used for the clustering task and the variational posterior associated with the generation task. Furthermore, we find that the new term increases resistance to posterior collapse. Theoretically, we demonstrate that our lower bound is a tighter approximation of the log-likelihood function. To address the second problem, we propose a graph update algorithm that reduces the over-segmentation and under-segmentation problems. We conduct several experiments to validate the merits of our approach. Our results show that the proposed method considerably improves the clustering quality compared to state-of-the-art VGAE models.

Most recent graph clustering methods have resorted to Graph Auto-Encoders (GAEs) to perform joint clustering and embedding learning. However, two critical issues have been overlooked. First, the accumulative error, inflicted by learning from noisy clustering assignments, degrades the effectiveness of the clustering model. This problem is called Feature Randomness. Second, reconstructing the adjacency matrix sets the model to learn irrelevant similarities for the clustering task. This problem is called Feature Drift. Furthermore, the theoretical relation between the aforementioned problems has not yet been investigated. We study these issues from two aspects: (1) there is a trade-off between Feature Randomness and Feature Drift when clustering and reconstruction are performed at the same level, and (2) the problem of Feature Drift is more pronounced for GAE models, compared with vanilla auto-encoder models. Thus, we reformulate the GAE-based clustering methodology. Our solution is two-fold. First, we propose a sampling operator$\Xi$that triggers a protection mechanism against Feature Randomness. Second, we propose an operator$\Upsilon$that triggers a correction mechanism against Feature Drift by gradually transforming the reconstructed graph into a clustering-oriented one. As principal advantages, our solution grants a considerable improvement in clustering effectiveness and can be easily tailored to GAE models.

We devise a graph attention network-based approach for learning a scene triangle mesh representation in order to estimate an image camera position in a dynamic environment. Previous approaches built a scene-dependent model that explicitly or implicitly embeds the structure of the scene. They use convolution neural networks or decision trees to establish 2D/3D-3D correspondences. Such a mapping overfits the target scene and does not generalize well to dynamic changes in the environment. Our work introduces a novel approach to solve the camera relocalization problem by using the available triangle mesh. Our 3D-3D matching framework consists of three blocks: (1) a graph neural network to compute the embedding of mesh vertices, (2) a convolution neural network to compute the embedding of grid cells defined on the RGB-D image, and (3) a neural network model to establish the correspondence between the two embeddings. These three components are trained end-to-end. To predict the final pose, we run the RANSAC algorithm to generate camera pose hypotheses, and we refine the prediction using the point-cloud representation. Our approach significantly improves the camera pose accuracy of the state-of-the-art method from 0.358 to 0.506 on the RIO10 benchmark for dynamic indoor camera relocalization.

To overcome the absence of concrete supervisory signals, deep clustering models construct their own labels based on self-supervision and pseudo-supervision. However, applying these techniques can cause Feature Randomness and Feature Drift. In this paper, we formally characterize these two new concepts. On one hand, Feature Randomness takes place when a considerable portion of the pseudo-labels is deemed to be random. In this regard, the trained model can learn non-representative features. On the other hand, Feature Drift takes place when the pseudo-supervised and the reconstruction losses are jointly minimized. While penalizing the reconstruction loss aims to preserve all the inherent data information, optimizing the embedded-clustering objective drops the latent between-cluster variances. Due to this compromise, the clustering-friendly representations can be easily drifted. In this context, we propose ADEC (Adversarial Deep Embedded Clustering) a novel autoencoder-based clustering model, which relies on a discriminator network to reduce random features while avoiding the drifting effect. Our new metrics$\Delta _{FR}$and$\Delta _{FD}$allows to, respectively, assess the level of Feature Randomness and Feature Drift. We empirically demonstrate the suitability of our model on handling these problems using benchmark real datasets. Experimental results validate that our model outperforms state-of-the-art autoencoder-based clustering methods.

Transfer function (TF) generation is a fundamental problem in direct volume rendering (DVR). A TF maps voxels to color and opacity values to reveal inner structures. Existing TF tools are complex and unintuitive for the users who are more likely to be medical professionals than computer scientists. In this article, we propose a novel image-centric method for TF generation where instead of complex tools, the user directly manipulates volume data to generate DVR. The user’s work is further simplified by presenting only the most informative volume slices for selection. Based on the selected parts, the voxels are classified using our novel sparse nonparametric support vector machine classifier, which combines both local and near-global distributional information of the training data. The voxel classes are mapped to aesthetically pleasing and distinguishable color and opacity values using harmonic colors. Experimental results on several benchmark datasets and a detailed user survey show the effectiveness of the proposed method.