Hence, extensiveresearch has been proposed to improvethisbound through richer distributions [55,53,36,13].

PAnoramic Semantic Segmentation (PASS) isanimportant task incomputer vision, as it enables semantic understanding of a 360 environment. Currently, most of existing works have focused on addressing the distortion issues in 2D panoramic images without considering spatial properties of indoor scene. This restricts PASS methods inperceiving contextual attributestodealwith theambiguity when working with monocular images. In this paper, we propose anovel approach for indoor panoramic semantic segmentation. Unlike previous works, we consider the panoramic image as a composition of segment groups:oversampled segments,representing planar structures suchasfloorsandceilings, and under-sampled segments, representing other scene elements.

Autoencoders (AEs) [1, 2] and its modern variants like the widely used variational autoencoders (VAEs) [3], are a powerful paradigm for self-supervised representation learning for generative modeling [4], compression [5], anomaly detection [6] or natural language processing [7]. Since autoencoders can learn low dimensional representations without requiring labeled data, they are particularly useful forcomputer vision taskswhere samples canbeveryhighdimensional making processing, transmitting, and search prohibitively expensive.

The Probabilistic Watershed isasemi-supervised learning algorithm applied on undirected graphs.

InfoGAN [10] in particular, learns an unsupervised disentangled representation by maximizing the mutual information between the discrete or continuous latent variables and the corresponding generated samples.

The ShinyHunters hacking group claims to have accessed Bumble and Match systems, highlighting how phishing attacks can threaten major online platforms.

However, such datasets also pose significant challenges in terms of computational and storage resources. It is important to note that these datasets may contain redundant or noisy samples that are either irrelevant or harmful to the model's performance.