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Found 15 papers, 5 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.
Leveraging the Context through Multi-Round Interactions for Jailbreaking Attacks
We contend that the prior context--the information preceding the attack query--plays a pivotal role in enabling potent Jailbreaking attacks.
Multilinear Operator Networks
On the other hand, Polynomial Networks is a class of models that does not require activation functions, but have yet to perform on par with modern architectures.
MonoNPHM: Dynamic Head Reconstruction from Monocular Videos
We present Monocular Neural Parametric Head Models (MonoNPHM) for dynamic 3D head reconstructions from monocular RGB videos.
HumanRF: High-Fidelity Neural Radiance Fields for Humans in Motion
To close the gap to production-level quality, we introduce HumanRF, a 4D dynamic neural scene representation that captures full-body appearance in motion from multi-view video input, and enables playback from novel, unseen viewpoints.
Learning Neural Parametric Head Models
We propose a novel 3D morphable model for complete human heads based on hybrid neural fields.
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.
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.
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.
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.
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.
