8,947 research outputs found
Algebraic Cayley Graphs over Finite Fields
A new algebraic Cayley graph is constructed using finite fields. Its
connectedness and diameter bound are studied via Weil's estimate for character
sums. These graphs provide a new source of expander graphs, extending classical
results of Chung
Multi-Domain Adaptation by Self-Supervised Learning for Speaker Verification
In real-world applications, speaker recognition models often face various
domain-mismatch challenges, leading to a significant drop in performance.
Although numerous domain adaptation techniques have been developed to address
this issue, almost all present methods focus on a simple configuration where
the model is trained in one domain and deployed in another. However, real-world
environments are often complex and may contain multiple domains, making the
methods designed for one-to-one adaptation suboptimal. In our paper, we propose
a self-supervised learning method to tackle this multi-domain adaptation
problem. Building upon the basic self-supervised adaptation algorithm, we
designed three strategies to make it suitable for multi-domain adaptation: an
in-domain negative sampling strategy, a MoCo-like memory bank scheme, and a
CORAL-like distribution alignment. We conducted experiments using VoxCeleb2 as
the source domain dataset and CN-Celeb1 as the target multi-domain dataset. Our
results demonstrate that our method clearly outperforms the basic
self-supervised adaptation method, which simply treats the data of CN-Celeb1 as
a single domain. Importantly, the improvement is consistent in nearly all
in-domain tests and cross-domain tests, demonstrating the effectiveness of our
proposed method.Comment: submitted to ICASSP 202
Multi-Modal Face Stylization with a Generative Prior
In this work, we introduce a new approach for artistic face stylization.
Despite existing methods achieving impressive results in this task, there is
still room for improvement in generating high-quality stylized faces with
diverse styles and accurate facial reconstruction. Our proposed framework,
MMFS, supports multi-modal face stylization by leveraging the strengths of
StyleGAN and integrates it into an encoder-decoder architecture. Specifically,
we use the mid-resolution and high-resolution layers of StyleGAN as the decoder
to generate high-quality faces, while aligning its low-resolution layer with
the encoder to extract and preserve input facial details. We also introduce a
two-stage training strategy, where we train the encoder in the first stage to
align the feature maps with StyleGAN and enable a faithful reconstruction of
input faces. In the second stage, the entire network is fine-tuned with
artistic data for stylized face generation. To enable the fine-tuned model to
be applied in zero-shot and one-shot stylization tasks, we train an additional
mapping network from the large-scale Contrastive-Language-Image-Pre-training
(CLIP) space to a latent space of fine-tuned StyleGAN. Qualitative and
quantitative experiments show that our framework achieves superior face
stylization performance in both one-shot and zero-shot stylization tasks,
outperforming state-of-the-art methods by a large margin
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