现有的未配对的低光图像增强方法更喜欢采用双向GAN框架，其中部署了两个CNN发生器以分别进行增强和降级。然而，这种数据驱动的模型忽略了低和正常光图像之间的变换的固有特性，导致不稳定的训练和伪像。在这里，我们建议利用可逆网络来增强前进过程中的低光图像，并与未配对的学习相反地降低正常光。然后将产生的和实际图像送入对抗性学习的鉴别器中。除了对抗性损失外，我们还设计各种损失功能，以确保培训的稳定性并保持更多图像细节。特别是，引入了可逆性损失以减轻过度暴露问题。此外，我们为低光图像提供了一种逐步的自我指导增强过程，对SOTA实现了良好的性能。

缺失数据的归责是在许多工程和科学应用中发挥着重要作用的任务。通常，这种缺失的数据来自传感器的限制或后处理转换误差的实验观察中。其他时间从计算机模拟中的数值和算法约束产生。本文的一个这样的实例和应用重点是风暴浪涌的数值模拟。模拟数据对应于感兴趣的地理领域内的多个保存点的时间序列浪涌预测，创建了浪涌点在空间且时间上大量相关的时空呈现问题，并且缺失的值区域在结构上分布随机的。最近，已经开发了机器学习技术，例如神经网络方法，并用于缺少数据归档任务。生成的对抗网（GAN）和基于GAN的技术是特别引起了无监督机器学习方法的关注。在这项研究中，通过应用卷积神经网络而不是完全连接的层来改善生成的对抗性归纳网（增益）性能，以更好地捕获数据的相关性并从相邻的浪涌点促进学习。对所研究的数据所需的方法的另一调整是考虑点作为附加特征的点的坐标，以通过卷积层提供更多信息。我们将所提出的方法称为卷积生成的对抗性普通网（CONV-GAIL）。通过考虑风暴浪涌数据所需的改进和适应来评估和与原始增益和其他一些技术进行评估，提出的方法的表现。结果表明，CONV增益比研究数据上的替代方法具有更好的性能。

野外的深度学习（DL）的成功采用需要模型：（1）紧凑，（2）准确，（3）强大的分布换档。不幸的是，同时满足这些要求的努力主要是不成功的。这提出了一个重要问题：无法创建紧凑，准确，强大的深神经网络（卡）基础？为了回答这个问题，我们对流行的模型压缩技术进行了大规模分析，该技术揭示了几种有趣模式。值得注意的是，与传统的修剪方法相比（例如，微调和逐渐修剪），我们发现“彩票式风格”方法令人惊讶地用于生产卡，包括二进制牌。具体而言，我们能够创建极其紧凑的卡，与其较大的对应物相比，具有类似的测试精度和匹配（或更好）的稳健性 - 仅通过修剪和（可选）量化。利用卡的紧凑性，我们开发了一种简单的域 - 自适应测试时间合并方法（卡片 - 甲板），它使用门控模块根据与测试样本的光谱相似性动态地选择相应的卡片。该拟议的方法建立了一个“赢得胜利”的卡片，即在CiFar-10-C精度（即96.8％标准和92.75％的鲁棒）和CiFar-100- C精度（80.6％标准和71.3％的稳健性），内存使用率比非压缩基线（Https://github.com/robustbench/robustbench提供的预制卡和卡片 - 甲板）。最后，我们为我们的理论支持提供了理论支持经验研究结果。

在地质不确定性下，快速同化监测数据以更新压力累积和压力累积和二氧化碳（CO2）羽流迁移的预测是地质碳储存中的一个具有挑战性的问题。具有高维参数空间的数据同化的高计算成本阻碍了商业规模库管理的快速决策。我们建议利用具有深度学习技术的多孔介质流动行为的物理理解，以开发快速历史匹配 - 水库响应预测工作流程。应用集合更顺畅的多数据同化框架，工作流程更新地质特性，并通过通过地震反转解释的压力历史和二氧化碳羽毛的量化不确定性来预测水库性能。由于这种工作流程中最具计算昂贵的组件是储层模拟，我们开发了代理模型，以在多孔注射下预测动态压力和CO2羽流量。代理模型采用深度卷积神经网络，具体地，宽的剩余网络和残留的U-Net。该工作流程针对代表碎屑货架沉积环境的扁平三维储层模型验证。智能处理应用于真正的3D储层模型中数量与单层储层模型之间的桥梁。工作流程可以在主流个人工作站上不到一小时内完成历史匹配和储库预测，在不到一小时内。

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.