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Found 54 papers, 11 papers with code
Efficient Learning of Multiple NLP Tasks via Collective Weight Factorization on BERT
We test our proposed method on finetuning multiple natural language understanding tasks by employing BERT-Large as an instantiation of the Transformer and the GLUE as the evaluation benchmark.
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.
Investigating Personalization Methods in Text to Music Generation
In this work, we investigate the personalization of text-to-music diffusion models in a few-shot setting.
Audio-visual video-to-speech synthesis with synthesized input audio
The implicit assumption of this task is that the sound signal is either missing or contains a high amount of noise/corruption such that it is not useful for processing.
Large-scale unsupervised audio pre-training for video-to-speech synthesis
Most established approaches to date involve a two-step process, whereby an intermediate representation from the video, such as a spectrogram, is extracted first and then passed to a vocoder to produce the raw audio.
Parts of Speech-Grounded Subspaces in Vision-Language Models
In this paper, we propose to separate representations of the different visual modalities in CLIP's joint vision-language space by leveraging the association between parts of speech and specific visual modes of variation (e. g. nouns relate to objects, adjectives describe appearance).
Generalization analysis of an unfolding network for analysis-based Compressed Sensing
Finally, the validity of our theory is assessed and numerical comparisons to a state-of-the-art unfolding network are made, on synthetic and real-world datasets.
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.
DECONET: an Unfolding Network for Analysis-based Compressed Sensing with Generalization Error Bounds
We present a new deep unfolding network for analysis-sparsity-based Compressed Sensing.
2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data Sets
Task 2 is centred on Brain-Computer Interfacing (BCI), addressing motor imagery decoding across both subjects and data sets.
Team Cogitat at NeurIPS 2021: Benchmarks for EEG Transfer Learning Competition
The second task required to transfer models trained on the subjects of one or more source motor imagery datasets to perform inference on two target datasets, providing a small set of personalized calibration data for multiple test subjects.
Cluster-guided Image Synthesis with Unconditional Models
The results highlight the ability of our approach to condition image generation on attributes like gender, pose and hair style on faces, as well as a variety of features on different object classes.
Conditional Generation Using Polynomial Expansions
We exhibit how CoPE can be trivially augmented to accept an arbitrary number of input variables.
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.
Defensive Tensorization
We propose defensive tensorization, an adversarial defence technique that leverages a latent high-order factorization of the network.
EEGminer: Discovering Interpretable Features of Brain Activity with Learnable Filters
Patterns of brain activity are associated with different brain processes and can be used to identify different brain states and make behavioral predictions.
Tensor Methods in Computer Vision and Deep Learning
Tensors, or multidimensional arrays, are data structures that can naturally represent visual data of multiple dimensions.
Augmenting Deep Classifiers with Polynomial Neural Networks
The efficacy of the proposed models is evaluated on standard image and audio classification benchmarks.
Ranked #2 on
Audio Classification
on Speech Commands
CoPE: Conditional image generation using Polynomial Expansions
We exhibit how CoPE can be trivially augmented to accept an arbitrary number of input variables.
MVP: Multivariate polynomials for conditional generation
The conditional variable can be discrete (e. g., a class label) or continuous (e. g., an input image) resulting into class-conditional (image) generation and image-to-image translation models, respectively.
Multilinear Latent Conditioning for Generating Unseen Attribute Combinations
Deep generative models rely on their inductive bias to facilitate generalization, especially for problems with high dimensional data, like images.
Deep Polynomial Neural Networks
We introduce three tensor decompositions that significantly reduce the number of parameters and show how they can be efficiently implemented by hierarchical neural networks.
Ranked #1 on
Face Recognition
on CALFW
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.
Investigating Bias in Deep Face Analysis: The KANFace Dataset and Empirical Study
In this work, we investigate the demographic bias of deep learning models in face recognition, age estimation, gender recognition and kinship verification.
$Π-$nets: Deep Polynomial Neural Networks
Deep Convolutional Neural Networks (DCNNs) is currently the method of choice both for generative, as well as for discriminative learning in computer vision and machine learning.
Ranked #1 on
Graph Representation Learning
on COMA
Speech-driven facial animation using polynomial fusion of features
Speech-driven facial animation involves using a speech signal to generate realistic videos of talking faces.
Defensive Tensorization: Randomized Tensor Parametrization for Robust Neural Networks
As deep neural networks become widely adopted for solving most problems in computer vision and audio-understanding, there are rising concerns about their potential vulnerability.
PolyGAN: High-Order Polynomial Generators
Generative Adversarial Networks (GANs) have become the gold standard when it comes to learning generative models for high-dimensional distributions.
Factorized Higher-Order CNNs with an Application to Spatio-Temporal Emotion Estimation
To alleviate this, one approach is to apply low-rank tensor decompositions to convolution kernels in order to compress the network and reduce its number of parameters.
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.
Tensor Dropout for Robust Learning
CNNs achieve remarkable performance by leveraging deep, over-parametrized architectures, trained on large datasets.
SEWA DB: A Rich Database for Audio-Visual Emotion and Sentiment Research in the Wild
Natural human-computer interaction and audio-visual human behaviour sensing systems, which would achieve robust performance in-the-wild are more needed than ever as digital devices are increasingly becoming an indispensable part of our life.
Modeling of Facial Aging and Kinship: A Survey
Computational facial models that capture properties of facial cues related to aging and kinship increasingly attract the attention of the research community, enabling the development of reliable methods for age progression, age estimation, age-invariant facial characterization, and kinship verification from visual data.
Visual Data Augmentation through Learning
In addition, the state-of-the-art data-driven methods demand a vast amount of data, hence a standard engineering trick employed is artificial data augmentation for instance by adding into the data cropped and (affinely) transformed images.
Side Information for Face Completion: a Robust PCA Approach
Robust principal component analysis (RPCA) is a powerful method for learning low-rank feature representation of various visual data.
Robust Kronecker Component Analysis
Dictionary learning and component analysis models are fundamental for learning compact representations that are relevant to a given task (feature extraction, dimensionality reduction, denoising, etc.).
Multi-Attribute Robust Component Analysis for Facial UV Maps
In this paper, we propose a novel component analysis technique that is suitable for facial UV maps containing a considerable amount of missing information and outliers, while additionally, incorporates knowledge from various attributes (such as age and identity).
GAGAN: Geometry-Aware Generative Adversarial Networks
Deep generative models learned through adversarial training have become increasingly popular for their ability to generate naturalistic image textures.
An Adversarial Neuro-Tensorial Approach For Learning Disentangled Representations
Several factors contribute to the appearance of an object in a visual scene, including pose, illumination, and deformation, among others.
Informed Non-convex Robust Principal Component Analysis with Features
We revisit the problem of robust principal component analysis with features acting as prior side information.
Robust Joint and Individual Variance Explained
Even though the CCA is a powerful tool, it has several drawbacks that render its application challenging for computer vision applications.
Learning the Multilinear Structure of Visual Data
To extract these modes of variations from visual data, several supervised methods, such as the TensorFaces, that rely on multilinear (tensor) decomposition (e. g., Higher Order SVD) have been developed.
Robust Kronecker-Decomposable Component Analysis for Low-Rank Modeling
In this paper, we introduce a new robust decomposition of images by combining ideas from sparse dictionary learning and PCP.
Side Information in Robust Principal Component Analysis: Algorithms and Applications
Robust Principal Component Analysis (RPCA) aims at recovering a low-rank subspace from grossly corrupted high-dimensional (often visual) data and is a cornerstone in many machine learning and computer vision applications.
Facial Expression Recognition
Facial Expression Recognition (FER)
+1
3D Face Morphable Models "In-the-Wild"
In this paper, we propose the first, to the best of our knowledge, "in-the-wild" 3DMM by combining a powerful statistical model of facial shape, which describes both identity and expression, with an "in-the-wild" texture model.
Ranked #3 on
3D Face Reconstruction
on Florence
(Average 3D Error metric)
Non-Negative Matrix Factorizations for Multiplex Network Analysis
Networks have been a general tool for representing, analyzing, and modeling relational data arising in several domains.
TensorLy: Tensor Learning in Python
In addition, using the deep-learning frameworks as backend allows users to easily design and train deep tensorized neural networks.
Robust Statistical Face Frontalization
The proposed method is assessed in frontal face reconstruction, face landmark localization, pose-invariant face recognition, and face verification in unconstrained conditions.
Face Flow
In this paper, we propose a method for the robust and efficient computation of multi-frame optical flow in an expressive sequence of facial images.
Automatic Construction Of Robust Spherical Harmonic Subspaces
In this paper we propose a method to automatically recover a class specific low dimensional spherical harmonic basis from a set of in-the-wild facial images.
Face frontalization for Alignment and Recognition
The proposed method is assessed in frontal face reconstruction (pose correction), face landmark localization, and pose-invariant face recognition and verification by conducting experiments on $6$ facial images databases.
RAPS: Robust and Efficient Automatic Construction of Person-Specific Deformable Models
Next, to correct the fittings of a generic model, image congealing (i. e., batch image aliment) is performed by employing only the learnt orthonormal subspace.
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.
