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Found 18 papers, 9 papers with code
Multilinear Mixture of Experts: Scalable Expert Specialization through Factorization
The Mixture of Experts (MoE) paradigm provides a powerful way to decompose inscrutable dense layers into smaller, modular computations often more amenable to human interpretation, debugging, and editability.
Locality-preserving Directions for Interpreting the Latent Space of Satellite Image GANs
We present a locality-aware method for interpreting the latent space of wavelet-based Generative Adversarial Networks (GANs), that can well capture the large spatial and spectral variability that is characteristic to satellite imagery.
Parts of Speech-Grounded Subspaces in Vision-Language Models
Latent image representations arising from vision-language models have proved immensely useful for a variety of downstream tasks.
Unsupervised Discovery of Semantic Concepts in Satellite Imagery with Style-based Wavelet-driven Generative Models
In recent years, considerable advancements have been made in the area of Generative Adversarial Networks (GANs), particularly with the advent of style-based architectures that address many key shortcomings - both in terms of modeling capabilities and network interpretability.
PandA: Unsupervised Learning of Parts and Appearances in the Feature Maps of GANs
Recent advances in the understanding of Generative Adversarial Networks (GANs) have led to remarkable progress in visual editing and synthesis tasks, capitalizing on the rich semantics that are embedded in the latent spaces of pre-trained GANs.
Tensor Component Analysis for Interpreting the Latent Space of GANs
This paper addresses the problem of finding interpretable directions in the latent space of pre-trained Generative Adversarial Networks (GANs) to facilitate controllable image synthesis.
Classification of Influenza Hemagglutinin Protein Sequences using Convolutional Neural Networks
This paper focuses on accurately predicting if an Influenza type A virus can infect specific hosts, and more specifically, Human, Avian and Swine hosts, using only the protein sequence of the HA gene.
Tensor Methods in Computer Vision and Deep Learning
Tensors, or multidimensional arrays, are data structures that can naturally represent visual data of multiple dimensions.
Enhancing Facial Data Diversity with Style-based Face Aging
By evaluating on several age-annotated datasets in both single- and cross-database experiments, we show that the proposed method outperforms state-of-the-art algorithms for age transfer, especially in the case of age groups that lie in the tails of the label distribution.
Multimodal Joint Emotion and Game Context Recognition in League of Legends Livestreams
Video game streaming provides the viewer with a rich set of audio-visual data, conveying information both with regards to the game itself, through game footage and audio, as well as the streamer's emotional state and behaviour via webcam footage and audio.
Adversarial Learning of Disentangled and Generalizable Representations for Visual Attributes
Recently, a multitude of methods for image-to-image translation have demonstrated impressive results on problems such as multi-domain or multi-attribute transfer.
Deep Unsupervised Multi-View Detection of Video Game Stream Highlights
We consider the problem of automatic highlight-detection in video game streams.
Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond
Automatic understanding of human affect using visual signals is of great importance in everyday human-machine interactions.
End-to-End Multimodal Emotion Recognition using Deep Neural Networks
The system is then trained in an end-to-end fashion where - by also taking advantage of the correlations of the each of the streams - we manage to significantly outperform the traditional approaches based on auditory and visual handcrafted features for the prediction of spontaneous and natural emotions on the RECOLA database of the AVEC 2016 research challenge on emotion recognition.
Deep Canonical Time Warping
Thus, they fail to capture complex, hierarchical non-linear representations which may prove to be beneficial towards the task of temporal alignment, particularly when dealing with multi-modal data (e. g., aligning visual and acoustic information).
Mnemonic Descent Method: A Recurrent Process Applied for End-To-End Face Alignment
Cascaded regression has recently become the method of choice for solving non-linear least squares problems such as deformable image alignment.
Robust Canonical Time Warping for the Alignment of Grossly Corrupted Sequences
The superiority of the proposed method against the state-of-the-art time alignment methods, namely the canonical time warping and the generalized time warping, is indicated by the experimental results on both synthetic and real datasets.
A Unified Framework for Probabilistic Component Analysis
We present a unifying framework which reduces the construction of probabilistic component analysis techniques to a mere selection of the latent neighbourhood, thus providing an elegant and principled framework for creating novel component analysis models as well as constructing probabilistic equivalents of deterministic component analysis methods.
