关于神经体系结构搜索（NAS）的现有研究主要集中于有效地搜索具有更好性能的网络体系结构。几乎没有取得进展，以系统地了解NAS搜索的架构是否对隐私攻击是强大的，而丰富的工作已经表明，人类设计的架构容易受到隐私攻击。在本文中，我们填补了这一空白，并系统地衡量了NAS体系结构的隐私风险。利用我们的测量研究中的见解，我们进一步探索了基于细胞的NAS架构的细胞模式，并评估细胞模式如何影响NAS搜索架构的隐私风险。通过广泛的实验，我们阐明了如何针对隐私攻击设计强大的NAS体系结构，还提供了一种通用方法，以了解NAS搜索的体系结构与其他隐私风险之间的隐藏相关性。

联合学习（FL）是一个蓬勃发展的分布式机器学习框架，其中中心参数服务器（PS）协调许多本地用户以训练全局一致的模型。传统的联合学习不可避免地依赖于具有PS的集中拓扑。因此，一旦PS失败，它将瘫痪。为了缓解如此单点故障，特别是在PS上，一些现有的工作已经提供了CDSGD和D-PSGD等分散的FL（DFL）实现，以便于分散拓扑中的流体。然而，这些方法仍存在一些问题，例如，在CDSGD中的用户最终模型和D-PSGD中的网络范围的模型平均必需品之间存在一些问题。为了解决这些缺陷，本文设计了一种作为DACFL的新DFL实现，其中每个用户使用自己的训练数据列举其模型，并通过对称和双随机矩阵将中间模型与其邻居交换。 DACFL将每个用户本地培训的进度视为离散时间过程，并采用第一个订单动态平均共识（FODAC）方法来跟踪\ Texit {平均模型}在没有PS的情况下。在本文中，我们还提供了DACFL的理论收敛性分析，即在I.I.D数据的前提下，以加强其合理性。 Mnist，Fashion-Mnist和CiFar-10的实验结果验证了我们在几间不变性和时变网络拓扑中的解决方案的可行性，并在大多数情况下声明DACFL优于D-PSGD和CDSGD。

Machine Unerning是在收到删除请求时从机器学习（ML）模型中删除某些培训数据的影响的过程。虽然直接而合法，但从划痕中重新训练ML模型会导致高计算开销。为了解决这个问题，在图像和文本数据的域中提出了许多近似算法，其中SISA是最新的解决方案。它将训练集随机分配到多个碎片中，并为每个碎片训练一个组成模型。但是，将SISA直接应用于图形数据可能会严重损害图形结构信息，从而导致的ML模型实用程序。在本文中，我们提出了Grapheraser，这是一种针对图形数据量身定制的新型机器学习框架。它的贡献包括两种新型的图形分区算法和一种基于学习的聚合方法。我们在五个现实世界图数据集上进行了广泛的实验，以说明Grapheraser的学习效率和模型实用程序。它可以实现2.06 $ \ times $（小数据集）至35.94 $ \ times $（大数据集）未学习时间的改进。另一方面，Grapheraser的实现最高62.5美元\％$更高的F1分数，我们提出的基于学习的聚合方法可达到高达$ 112 \％$ $ F1分数。 github.com/minchen00/graph-unlearning}。}。}

Masked image modeling (MIM) performs strongly in pre-training large vision Transformers (ViTs). However, small models that are critical for real-world applications cannot or only marginally benefit from this pre-training approach. In this paper, we explore distillation techniques to transfer the success of large MIM-based pre-trained models to smaller ones. We systematically study different options in the distillation framework, including distilling targets, losses, input, network regularization, sequential distillation, etc, revealing that: 1) Distilling token relations is more effective than CLS token- and feature-based distillation; 2) An intermediate layer of the teacher network as target perform better than that using the last layer when the depth of the student mismatches that of the teacher; 3) Weak regularization is preferred; etc. With these findings, we achieve significant fine-tuning accuracy improvements over the scratch MIM pre-training on ImageNet-1K classification, using all the ViT-Tiny, ViT-Small, and ViT-base models, with +4.2%/+2.4%/+1.4% gains, respectively. Our TinyMIM model of base size achieves 52.2 mIoU in AE20K semantic segmentation, which is +4.1 higher than the MAE baseline. Our TinyMIM model of tiny size achieves 79.6% top-1 accuracy on ImageNet-1K image classification, which sets a new record for small vision models of the same size and computation budget. This strong performance suggests an alternative way for developing small vision Transformer models, that is, by exploring better training methods rather than introducing inductive biases into architectures as in most previous works. Code is available at https://github.com/OliverRensu/TinyMIM.

In this paper, we propose a robust 3D detector, named Cross Modal Transformer (CMT), for end-to-end 3D multi-modal detection. Without explicit view transformation, CMT takes the image and point clouds tokens as inputs and directly outputs accurate 3D bounding boxes. The spatial alignment of multi-modal tokens is performed implicitly, by encoding the 3D points into multi-modal features. The core design of CMT is quite simple while its performance is impressive. CMT obtains 73.0% NDS on nuScenes benchmark. Moreover, CMT has a strong robustness even if the LiDAR is missing. Code will be released at https://github.com/junjie18/CMT.

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.

Automatic music generation with artificial intelligence typically requires a large amount of data which is hard to obtain for many less common genres and musical instruments. To tackle this issue, we present ongoing work and preliminary findings on the possibility for deep models to transfer knowledge from language to music, by finetuning large language models pre-trained on a massive text corpus on only hundreds of MIDI files of drum performances. We show that by doing so, one of the largest, state-of-the-art models (GPT3) is capable of generating reasonable drum grooves, while models that are not pre-trained (Transformer) shows no such ability beyond naive repetition. Evaluating generated music is a challenging task, more so is evaluating drum grooves with little precedence in literature. Hence, we propose a tailored structural evaluation method and analyze drum grooves produced by GPT3 compared to those played by human professionals, exposing the strengths and weaknesses of such generation by language-to-music transfer. Our findings suggest that language-to-music transfer learning with large language models is viable and promising.

Few Shot Instance Segmentation (FSIS) requires models to detect and segment novel classes with limited several support examples. In this work, we explore a simple yet unified solution for FSIS as well as its incremental variants, and introduce a new framework named Reference Twice (RefT) to fully explore the relationship between support/query features based on a Transformer-like framework. Our key insights are two folds: Firstly, with the aid of support masks, we can generate dynamic class centers more appropriately to re-weight query features. Secondly, we find that support object queries have already encoded key factors after base training. In this way, the query features can be enhanced twice from two aspects, i.e., feature-level and instance-level. In particular, we firstly design a mask-based dynamic weighting module to enhance support features and then propose to link object queries for better calibration via cross-attention. After the above steps, the novel classes can be improved significantly over our strong baseline. Additionally, our new framework can be easily extended to incremental FSIS with minor modification. When benchmarking results on the COCO dataset for FSIS, gFSIS, and iFSIS settings, our method achieves a competitive performance compared to existing approaches across different shots, e.g., we boost nAP by noticeable +8.2/+9.4 over the current state-of-the-art FSIS method for 10/30-shot. We further demonstrate the superiority of our approach on Few Shot Object Detection. Code and model will be available.

Graph Neural Networks (GNNs) have shown satisfying performance on various graph learning tasks. To achieve better fitting capability, most GNNs are with a large number of parameters, which makes these GNNs computationally expensive. Therefore, it is difficult to deploy them onto edge devices with scarce computational resources, e.g., mobile phones and wearable smart devices. Knowledge Distillation (KD) is a common solution to compress GNNs, where a light-weighted model (i.e., the student model) is encouraged to mimic the behavior of a computationally expensive GNN (i.e., the teacher GNN model). Nevertheless, most existing GNN-based KD methods lack fairness consideration. As a consequence, the student model usually inherits and even exaggerates the bias from the teacher GNN. To handle such a problem, we take initial steps towards fair knowledge distillation for GNNs. Specifically, we first formulate a novel problem of fair knowledge distillation for GNN-based teacher-student frameworks. Then we propose a principled framework named RELIANT to mitigate the bias exhibited by the student model. Notably, the design of RELIANT is decoupled from any specific teacher and student model structures, and thus can be easily adapted to various GNN-based KD frameworks. We perform extensive experiments on multiple real-world datasets, which corroborates that RELIANT achieves less biased GNN knowledge distillation while maintaining high prediction utility.