Speech enhancement significantly improves the clarity and intelligibility of
speech in noisy environments, improving communication and listening
experiences. In this paper, we introduce a novel pretraining feature-guided
diffusion model tailored for efficient speech enhancement, addressing the
limitations of existing discriminative and generative models. By integrating
spectral features into a variational autoencoder (VAE) and leveraging
pre-trained features for guidance during the reverse process, coupled with the
utilization of the deterministic discrete integration method (DDIM) to
streamline sampling steps, our model improves efficiency and speech enhancement
quality. Demonstrating state-of-the-art results on two public datasets with
different SNRs, our model outshines other baselines in efficiency and
robustness. The proposed method not only optimizes performance but also
enhances practical deployment capabilities, without increasing computational
demands.

通过整合频谱特征到变分自编码器（VAE）中，并在反向过程中利用预训练特征进行引导，结合确定性离散积分方法（DDIM）来简化抽样步骤，我们的模型提高了效率和语音增强质量，同时在两个不同信噪比的公共数据集上展示出最先进的结果，超越了其他基线方法在效率和稳健性方面。所提出的方法不仅优化了性能，还增强了实际部署能力，而不增加计算需求。

预训练特征引导扩散模型用于语音增强

Pre-training Feature Guided Diffusion Model for Speech Enhancement

Single-model systems often suffer from deficiencies in tasks such as speaker
verification (SV) and image classification, relying heavily on partial prior
knowledge during decision-making, resulting in suboptimal performance. Although
multi-model fusion (MMF) can mitigate some of these issues, redundancy in
learned representations may limits improvements. To this end, we propose an
adversarial complementary representation learning (ACoRL) framework that
enables newly trained models to avoid previously acquired knowledge, allowing
each individual component model to learn maximally distinct, complementary
representations. We make three detailed explanations of why this works and
experimental results demonstrate that our method more efficiently improves
performance compared to traditional MMF. Furthermore, attribution analysis
validates the model trained under ACoRL acquires more complementary knowledge,
highlighting the efficacy of our approach in enhancing efficiency and
robustness across tasks.

通过对单模型系统和多模型融合的研究，我们提出了一个基于对抗性互补表示学习框架，该框架使新训练的模型能够避免先前获取的知识，使每个单独的组成模型能够学习最大程度不同、互补的表示方法，并通过实验结果证明我们的方法相比传统的多模型融合能更有效地提高性能。此外，归因分析验证了在 ACoRL 下训练的模型获取了更多的互补知识，突显了我们的方法在提高效率和鲁棒性方面的有效性。

有效多模型融合与对抗互补表示学习

Efficient Multi-Model Fusion with Adversarial Complementary  Representation Learning

Estimating the rigid transformation with 6 degrees of freedom based on a
putative 3D correspondence set is a crucial procedure in point cloud
registration. Existing correspondence identification methods usually lead to
large outlier ratios ($>$ 95 $\%$ is common), underscoring the significance of
robust registration methods. Many researchers turn to parameter search-based
strategies (e.g., Branch-and-Bround) for robust registration. Although related
methods show high robustness, their efficiency is limited to the
high-dimensional search space. This paper proposes a heuristics-guided
parameter search strategy to accelerate the search while maintaining high
robustness. We first sample some correspondences (i.e., heuristics) and then
just need to sequentially search the feasible regions that make each sample an
inlier. Our strategy largely reduces the search space and can guarantee
accuracy with only a few inlier samples, therefore enjoying an excellent
trade-off between efficiency and robustness. Since directly parameterizing the
6-dimensional nonlinear feasible region for efficient search is intractable, we
construct a three-stage decomposition pipeline to reparameterize the feasible
region, resulting in three lower-dimensional sub-problems that are easily
solvable via our strategy. Besides reducing the searching dimension, our
decomposition enables the leverage of 1-dimensional interval stabbing at all
three stages for searching acceleration. Moreover, we propose a valid sampling
strategy to guarantee our sampling effectiveness, and a compatibility
verification setup to further accelerate our search. Extensive experiments on
both simulated and real-world datasets demonstrate that our approach exhibits
comparable robustness with state-of-the-art methods while achieving a
significant efficiency boost.

通过启发式参数搜索策略，该论文提出了一种在点云配准中加速搜索而又保持高鲁棒性的方法，通过对可行域的三阶分解和一维区间刺探的利用，降低了搜索维度，并展示了与最先进方法相当的鲁棒性和显著的效率提升。