Benefiting from its single-photon sensitivity, single-photon avalanche diode (SPAD) array has been widely applied in various fields such as fluorescence lifetime imaging and quantum computing. However, large-scale high-fidelity single-photon imaging remains a big challenge, due to the complex hardware manufacture craft and heavy noise disturbance of SPAD arrays. In this work, we introduce deep learning into SPAD, enabling super-resolution single-photon imaging over an order of magnitude, with significant enhancement of bit depth and imaging quality. We first studied the complex photon flow model of SPAD electronics to accurately characterize multiple physical noise sources, and collected a real SPAD image dataset (64 $\times$ 32 pixels, 90 scenes, 10 different bit depth, 3 different illumination flux, 2790 images in total) to calibrate noise model parameters. With this real-world physical noise model, we for the first time synthesized a large-scale realistic single-photon image dataset (image pairs of 5 different resolutions with maximum megapixels, 17250 scenes, 10 different bit depth, 3 different illumination flux, 2.6 million images in total) for subsequent network training. To tackle the severe super-resolution challenge of SPAD inputs with low bit depth, low resolution, and heavy noise, we further built a deep transformer network with a content-adaptive self-attention mechanism and gated fusion modules, which can dig global contextual features to remove multi-source noise and extract full-frequency details. We applied the technique on a series of experiments including macroscopic and microscopic imaging, microfluidic inspection, and Fourier ptychography. The experiments validate the technique's state-of-the-art super-resolution SPAD imaging performance, with more than 5 dB superiority on PSNR compared to the existing methods.

在弱光环境下，手持式摄影在长时间的曝光设置下遭受了严重的相机震动。尽管现有的Deblurry算法在暴露良好的模糊图像上表现出了令人鼓舞的性能，但它们仍然无法应对低光快照。在实用的低光脱毛中，复杂的噪声和饱和区是两个主导挑战。在这项工作中，我们提出了一种称为图像的新型非盲脱毛方法，并具有特征空间Wiener Deonervolution网络（Infwide），以系统地解决这些问题。在算法设计方面，Infwide提出了一个两分支的架构，该体系结构明确消除了噪声并幻觉，使图像空间中的饱和区域抑制了特征空间中的响起文物，并将两个互补输出与一个微妙的多尺度融合网络集成在一起高质量的夜间照片浮雕。为了进行有效的网络培训，我们设计了一组损失功能，集成了前向成像模型和向后重建，以形成近环的正则化，以确保深神经网络的良好收敛性。此外，为了优化Infwide在实际弱光条件下的适用性，采用基于物理过程的低光噪声模型来合成现实的嘈杂夜间照片进行模型训练。利用传统的Wiener Deonervolution算法的身体驱动的特征并引起了深层神经网络的表示能力，Infwide可以恢复细节，同时抑制在脱毛期间的不愉快的人工制品。关于合成数据和实际数据的广泛实验证明了所提出的方法的出色性能。

使用单像素检测，联合优化编码和解码的端到端神经网络可以实现高精度成像和高电平语义传感。然而，对于不同的采样率，大规模网络需要重新培训，这是呈现的呈现和计算消耗。在这封信中，我们报告了一种加权优化技术，用于动态速率自适应单像素成像和感应，只需要培训网络一次可用于任何采样率的时间一次。具体地，我们在编码过程中引入一种新的加权方案，以表征不同的模式的调制效率。虽然网络以高采样速率训练，但是迭代地更新调制模式和相应的权重，这在融合时产生最佳排名编码串。在实验实施方案中，采用最高重量的最佳模式系列用于光调制，从而实现高效的成像和感测。报告的策略节省了现有动态单像素网络所需另一种低速速率网络的额外培训，这进一步加倍训练效率。验证了Mnist DataSet上的实验，通过采样率为1的网络培训，平均成像PSNR为0.1采样率达到23.50 dB，并且图像的图像分类精度达到高达95.00 \％，以0.03的采样率达到95.00 \％ 97.91 \％以0.1的采样率。

最近开发的图像无感测技术维持了灯具硬件和软件的优点，该软件已应用于简单的目标分类和运动跟踪。但是，在实际应用中，通常存在多个目标在视野中，其中现有的试验未能产生多语义信息。在这封信中，我们报告了一种新颖的自由感测技术，首次解决多目标识别挑战。与无图像单像素网络的卷积层堆叠不同，报告的CRNN网络实用程序双向LSTM架构可以同时预测多个字符的分布。框架可以捕获远程依赖项，提供多个字符的高识别精度。我们证明了该技术在车牌检测中的有效性，其识别精度为5％的采样率，具有高于100 FPS刷新率。

水下杂质的光吸收和散射导致水下较差的水下成像质量。现有的基于数据驱动的基于数据的水下图像增强（UIE）技术缺乏包含各种水下场景和高保真参考图像的大规模数据集。此外，不同颜色通道和空间区域的不一致衰减不完全考虑提升增强。在这项工作中，我们构建了一个大规模的水下图像（LSUI）数据集，包括5004个图像对，并报告了一个U形变压器网络，其中变压器模型首次引入UIE任务。 U形变压器与通道 - 方面的多尺度特征融合变压器（CMSFFT）模块和空间全局功能建模变压器（SGFMT）模块集成在一起，可使用更多地加强网络对色频道和空间区域的关注严重衰减。同时，为了进一步提高对比度和饱和度，在人类视觉原理之后，设计了组合RGB，实验室和LCH颜色空间的新型损失函数。可用数据集的广泛实验验证了报告的技术的最先进性能，具有超过2dB的优势。

Denoising Diffusion Probabilistic Models (DDPMs) are emerging in text-to-speech (TTS) synthesis because of their strong capability of generating high-fidelity samples. However, their iterative refinement process in high-dimensional data space results in slow inference speed, which restricts their application in real-time systems. Previous works have explored speeding up by minimizing the number of inference steps but at the cost of sample quality. In this work, to improve the inference speed for DDPM-based TTS model while achieving high sample quality, we propose ResGrad, a lightweight diffusion model which learns to refine the output spectrogram of an existing TTS model (e.g., FastSpeech 2) by predicting the residual between the model output and the corresponding ground-truth speech. ResGrad has several advantages: 1) Compare with other acceleration methods for DDPM which need to synthesize speech from scratch, ResGrad reduces the complexity of task by changing the generation target from ground-truth mel-spectrogram to the residual, resulting into a more lightweight model and thus a smaller real-time factor. 2) ResGrad is employed in the inference process of the existing TTS model in a plug-and-play way, without re-training this model. We verify ResGrad on the single-speaker dataset LJSpeech and two more challenging datasets with multiple speakers (LibriTTS) and high sampling rate (VCTK). Experimental results show that in comparison with other speed-up methods of DDPMs: 1) ResGrad achieves better sample quality with the same inference speed measured by real-time factor; 2) with similar speech quality, ResGrad synthesizes speech faster than baseline methods by more than 10 times. Audio samples are available at https://resgrad1.github.io/.

Diffusion models have achieved state-of-the-art synthesis quality on visual and audio tasks, and recent works adapt them to textual data by diffusing on the embedding space. But the difference between the continuous data space and the embedding space raises challenges to the diffusion model, which have not been carefully explored. In this paper, we conduct systematic studies and analyze the challenges threefold. Firstly, the data distribution is learnable for embeddings, which may lead to the collapse of the loss function. Secondly, as the norm of embedding varies between popular and rare words, adding the same noise scale will lead to sub-optimal results. In addition, we find that noises sampled from a standard Gaussian distribution may distract the diffusion process. To solve the above challenges, we propose Difformer, a denoising diffusion probabilistic model based on Transformer, which consists of three techniques including utilizing an anchor loss function, a layer normalization module for embeddings, and a norm factor to the Gaussian noise. All techniques are complementary to each other and critical to boosting the model performance together. Experiments are conducted on benchmark datasets over two seminal text generation tasks including machine translation and text summarization. The results show that Difformer significantly outperforms the embedding diffusion baselines, while achieving competitive results with strong autoregressive baselines.

While mislabeled or ambiguously-labeled samples in the training set could negatively affect the performance of deep models, diagnosing the dataset and identifying mislabeled samples helps to improve the generalization power. Training dynamics, i.e., the traces left by iterations of optimization algorithms, have recently been proved to be effective to localize mislabeled samples with hand-crafted features. In this paper, beyond manually designed features, we introduce a novel learning-based solution, leveraging a noise detector, instanced by an LSTM network, which learns to predict whether a sample was mislabeled using the raw training dynamics as input. Specifically, the proposed method trains the noise detector in a supervised manner using the dataset with synthesized label noises and can adapt to various datasets (either naturally or synthesized label-noised) without retraining. We conduct extensive experiments to evaluate the proposed method. We train the noise detector based on the synthesized label-noised CIFAR dataset and test such noise detector on Tiny ImageNet, CUB-200, Caltech-256, WebVision and Clothing1M. Results show that the proposed method precisely detects mislabeled samples on various datasets without further adaptation, and outperforms state-of-the-art methods. Besides, more experiments demonstrate that the mislabel identification can guide a label correction, namely data debugging, providing orthogonal improvements of algorithm-centric state-of-the-art techniques from the data aspect.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Massively multi-task learning with large language models has recently made substantial progress on few-shot generalization. However, this is usually performed in a centralized learning fashion, ignoring the privacy sensitivity issue of (annotated) data used in multiple tasks. To mitigate this issue, we propose FewFedWeight, a few-shot federated learning framework across multiple tasks, to achieve the best of both worlds: privacy preservation and cross-task generalization. FewFedWeight trains client models in isolated devices without sharing data. It broadcasts the global model in the server to each client and produces pseudo data for clients so that knowledge from the global model can be explored to enhance few-shot learning of each client model. An energy-based algorithm is further proposed to weight pseudo samples in order to reduce the negative impact of noise from the generated pseudo data. Adaptive model weights of client models are also tuned according to their performance. We use these model weights to dynamically aggregate client models to update the global model. Experiments on 118 NLP tasks show that FewFedWeight can significantly improve the performance of client models on 61% tasks with an average performance improvement rate of 30.5% over the baseline and substantially outperform FedAvg and other decentralized learning methods.