最近的基于学习的图像雨和噪声衰减的繁荣主要是由于精心设计的神经网络架构和大型标记数据集。但是，我们发现当前的图像雨和噪声去除方法导致图像的利用率低。为了减轻对大型标签数据集的依赖，我们提出了基于引入的补丁分析策略的任务驱动的图像雨和噪声（TRNR）。补丁分析策略提供了具有各种空间和统计特性的图像贴片，用于培训，并已被验证以增加图像的利用率。此外，补丁分析策略激励我们考虑学习图像雨和噪声去除任务驱动而不是数据驱动。因此，我们介绍了TRNR的N频率-K射击学习任务。每个N频率-K-Shot学习任务基于包含补丁分析策略采样的NK图像修补的微小数据集。 TRNR使神经网络能够从足够的数据以外的丰富N频率-K射击学习任务中学习。为了验证TRNR的有效性，我们建立了一个浅色多尺度残差网络（MSRESNet），具有约0.9米的参数来学习图像雨量拆卸，并使用简单的RESET与大约1.2M参数配合DNNET进行盲目高斯噪声删除，有一些图像（例如，20.0％的Rain100h培训赛车组）。实验结果表明，TRNR使MSRESNet能够从更少的图像中学到更好的学习。此外，MSRESNet和DNNET利用TRNR获得的性能比大多数最近的深度学习方法在大型标记数据集上受过训练的数据驱动。这些实验结果证实了所提出的TRNR的有效性和优越性。 TRNR的代码将很快公开。

虽然网络稀疏作为克服神经网络大小的有希望的方向，但它仍然是保持模型准确性的开放问题，并在一般CPU上实现了显着的加速。在本文中，我们提出了一篇新颖的1美元\ Times N $块稀疏模式（块修剪）的概念来打破这种限制。特别是，具有相同输入通道索引的连续$ N $输出内核被分组为一个块，该块用作我们修剪模式的基本修剪粒度。我们的$ 1 \ times n $ sparsity模式prunes这些块被认为不重要。我们还提供过滤器重新排列的工作流程，首先重新排列输出通道尺寸中的权重矩阵，以获得更具影响力的块，以便精度改进，然后将类似的重新排列到输入通道维度中的下一层权重，以确保正确的卷积操作。此外，可以通过并行化块 - 方向的矢量化操作实现在我们的$ 1 \ Times N $块稀疏之后的输出计算，从而导致总基于CPU的平台上的显着加速。通过对ILSVRC-2012的实验证明了我们修剪模式的功效。例如，在50％的稀疏性和$ n = 4 $的情况下，我们的模式在MobileNet-V2的前1个精度的过滤器修剪中获得了大约3.0％的改进。同时，它在Cortex-A7 CPU上获得56.04ms推断，超过体重修剪。代码可在https://github.com/lmbxmu/1xn处获得。

本文提出了一种任何时间的超分辨率方法（ARM），以解决过度参数化的单图像超分辨率（SISR）模型。我们的手臂是由三个观察结果激励的：（1）不同图像贴片的性能随不同大小的SISR网络而变化。 （2）计算开销与重建图像的性能之间存在权衡。 （3）给定输入图像，其边缘信息可以是估计其PSNR的有效选择。随后，我们训练包含不同尺寸的SISR子网的手臂超网，以处理各种复杂性的图像斑块。为此，我们构建了一个边缘到PSNR查找表，该表将图像补丁的边缘分数映射到每个子网的PSNR性能，以及子网的一组计算成本。在推论中，图像贴片单独分配给不同的子网，以获得更好的计算绩效折衷。此外，每个SISR子网都共享手臂超网的权重，因此不引入额外的参数。多个子网的设置可以很好地使SISR模型的计算成本适应动态可用的硬件资源，从而可以随时使用SISR任务。对不同大小的分辨率数据集的广泛实验和流行的SISR网络作为骨架验证了我们的手臂的有效性和多功能性。源代码可在https://github.com/chenbong/arm-net上找到。

The mainstream approach for filter pruning is usually either to force a hard-coded importance estimation upon a computation-heavy pretrained model to select "important" filters, or to impose a hyperparameter-sensitive sparse constraint on the loss objective to regularize the network training. In this paper, we present a novel filter pruning method, dubbed dynamic-coded filter fusion (DCFF), to derive compact CNNs in a computation-economical and regularization-free manner for efficient image classification. Each filter in our DCFF is firstly given an inter-similarity distribution with a temperature parameter as a filter proxy, on top of which, a fresh Kullback-Leibler divergence based dynamic-coded criterion is proposed to evaluate the filter importance. In contrast to simply keeping high-score filters in other methods, we propose the concept of filter fusion, i.e., the weighted averages using the assigned proxies, as our preserved filters. We obtain a one-hot inter-similarity distribution as the temperature parameter approaches infinity. Thus, the relative importance of each filter can vary along with the training of the compact CNN, leading to dynamically changeable fused filters without both the dependency on the pretrained model and the introduction of sparse constraints. Extensive experiments on classification benchmarks demonstrate the superiority of our DCFF over the compared counterparts. For example, our DCFF derives a compact VGGNet-16 with only 72.77M FLOPs and 1.06M parameters while reaching top-1 accuracy of 93.47% on CIFAR-10. A compact ResNet-50 is obtained with 63.8% FLOPs and 58.6% parameter reductions, retaining 75.60% top-1 accuracy on ILSVRC-2012. Our code, narrower models and training logs are available at https://github.com/lmbxmu/DCFF.

Dataset distillation has emerged as a prominent technique to improve data efficiency when training machine learning models. It encapsulates the knowledge from a large dataset into a smaller synthetic dataset. A model trained on this smaller distilled dataset can attain comparable performance to a model trained on the original training dataset. However, the existing dataset distillation techniques mainly aim at achieving the best trade-off between resource usage efficiency and model utility. The security risks stemming from them have not been explored. This study performs the first backdoor attack against the models trained on the data distilled by dataset distillation models in the image domain. Concretely, we inject triggers into the synthetic data during the distillation procedure rather than during the model training stage, where all previous attacks are performed. We propose two types of backdoor attacks, namely NAIVEATTACK and DOORPING. NAIVEATTACK simply adds triggers to the raw data at the initial distillation phase, while DOORPING iteratively updates the triggers during the entire distillation procedure. We conduct extensive evaluations on multiple datasets, architectures, and dataset distillation techniques. Empirical evaluation shows that NAIVEATTACK achieves decent attack success rate (ASR) scores in some cases, while DOORPING reaches higher ASR scores (close to 1.0) in all cases. Furthermore, we conduct a comprehensive ablation study to analyze the factors that may affect the attack performance. Finally, we evaluate multiple defense mechanisms against our backdoor attacks and show that our attacks can practically circumvent these defense mechanisms.

Blind image quality assessment (BIQA) remains challenging due to the diversity of distortion and image content variation, which complicate the distortion patterns crossing different scales and aggravate the difficulty of the regression problem for BIQA. However, existing BIQA methods often fail to consider multi-scale distortion patterns and image content, and little research has been done on learning strategies to make the regression model produce better performance. In this paper, we propose a simple yet effective Progressive Multi-Task Image Quality Assessment (PMT-IQA) model, which contains a multi-scale feature extraction module (MS) and a progressive multi-task learning module (PMT), to help the model learn complex distortion patterns and better optimize the regression issue to align with the law of human learning process from easy to hard. To verify the effectiveness of the proposed PMT-IQA model, we conduct experiments on four widely used public datasets, and the experimental results indicate that the performance of PMT-IQA is superior to the comparison approaches, and both MS and PMT modules improve the model's performance.

The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research. To address these issues, we propose a Multi-Relational Graph-Based Twitter Account Detection Benchmark (MGTAB), the first standardized graph-based benchmark for account detection. To our knowledge, MGTAB was built based on the largest original data in the field, with over 1.55 million users and 130 million tweets. MGTAB contains 10,199 expert-annotated users and 7 types of relationships, ensuring high-quality annotation and diversified relations. In MGTAB, we extracted the 20 user property features with the greatest information gain and user tweet features as the user features. In addition, we performed a thorough evaluation of MGTAB and other public datasets. Our experiments found that graph-based approaches are generally more effective than feature-based approaches and perform better when introducing multiple relations. By analyzing experiment results, we identify effective approaches for account detection and provide potential future research directions in this field. Our benchmark and standardized evaluation procedures are freely available at: https://github.com/GraphDetec/MGTAB.

Given the increasingly intricate forms of partial differential equations (PDEs) in physics and related fields, computationally solving PDEs without analytic solutions inevitably suffers from the trade-off between accuracy and efficiency. Recent advances in neural operators, a kind of mesh-independent neural-network-based PDE solvers, have suggested the dawn of overcoming this challenge. In this emerging direction, Koopman neural operator (KNO) is a representative demonstration and outperforms other state-of-the-art alternatives in terms of accuracy and efficiency. Here we present KoopmanLab, a self-contained and user-friendly PyTorch module of the Koopman neural operator family for solving partial differential equations. Beyond the original version of KNO, we develop multiple new variants of KNO based on different neural network architectures to improve the general applicability of our module. These variants are validated by mesh-independent and long-term prediction experiments implemented on representative PDEs (e.g., the Navier-Stokes equation and the Bateman-Burgers equation) and ERA5 (i.e., one of the largest high-resolution data sets of global-scale climate fields). These demonstrations suggest the potential of KoopmanLab to be considered in diverse applications of partial differential equations.

A recent study has shown a phenomenon called neural collapse in that the within-class means of features and the classifier weight vectors converge to the vertices of a simplex equiangular tight frame at the terminal phase of training for classification. In this paper, we explore the corresponding structures of the last-layer feature centers and classifiers in semantic segmentation. Based on our empirical and theoretical analysis, we point out that semantic segmentation naturally brings contextual correlation and imbalanced distribution among classes, which breaks the equiangular and maximally separated structure of neural collapse for both feature centers and classifiers. However, such a symmetric structure is beneficial to discrimination for the minor classes. To preserve these advantages, we introduce a regularizer on feature centers to encourage the network to learn features closer to the appealing structure in imbalanced semantic segmentation. Experimental results show that our method can bring significant improvements on both 2D and 3D semantic segmentation benchmarks. Moreover, our method ranks 1st and sets a new record (+6.8% mIoU) on the ScanNet200 test leaderboard. Code will be available at https://github.com/dvlab-research/Imbalanced-Learning.

Benefiting from the intrinsic supervision information exploitation capability, contrastive learning has achieved promising performance in the field of deep graph clustering recently. However, we observe that two drawbacks of the positive and negative sample construction mechanisms limit the performance of existing algorithms from further improvement. 1) The quality of positive samples heavily depends on the carefully designed data augmentations, while inappropriate data augmentations would easily lead to the semantic drift and indiscriminative positive samples. 2) The constructed negative samples are not reliable for ignoring important clustering information. To solve these problems, we propose a Cluster-guided Contrastive deep Graph Clustering network (CCGC) by mining the intrinsic supervision information in the high-confidence clustering results. Specifically, instead of conducting complex node or edge perturbation, we construct two views of the graph by designing special Siamese encoders whose weights are not shared between the sibling sub-networks. Then, guided by the high-confidence clustering information, we carefully select and construct the positive samples from the same high-confidence cluster in two views. Moreover, to construct semantic meaningful negative sample pairs, we regard the centers of different high-confidence clusters as negative samples, thus improving the discriminative capability and reliability of the constructed sample pairs. Lastly, we design an objective function to pull close the samples from the same cluster while pushing away those from other clusters by maximizing and minimizing the cross-view cosine similarity between positive and negative samples. Extensive experimental results on six datasets demonstrate the effectiveness of CCGC compared with the existing state-of-the-art algorithms.