混合方案表明混合一对样品以创造增强的训练样本，并最近获得了相当大的关注，以提高神经网络的普遍性。混合的简单和广泛使用的扩展是与区域辍学方法相结合：从样品中除去随机贴片并用另一个样品的特征替换。尽管它们的简单性和有效性，但这些方法易于由于它们的随机性而产生有害样品。为了解决这个问题，最近提出了“最大显着性”策略：只选择最具信息性的功能以防止这种现象。然而，他们现在缺乏样品多样化，因为它们总是确定具有最大显着性的区域，将偏置注入增强数据。在本文中，我们展示了一种新颖，简单的混合变体，捕获了两个世界的最佳变化。我们的想法是两倍。通过将特征的随机抽查和“将它们嫁接到另一个样本”，我们的方法有效地产生了多样化但有意义的样本。其第二种成分是通过以显着校准的方式混合标签来生产接枝样品的标签，这整流了随机抽样程序引入的监督误导。我们在CiFar，微小想象成和Imagenet数据集下的实验表明，我们的方案不仅在分类准确性方面优于当前的最先进的增强策略，但在数据损坏等压力条件下也是优越的对象遮挡。

尽管能够与过度能力网络概括，但深神经网络通常会学会滥用数据中的虚假偏见而不是使用实际的任务相关信息。由于此类快捷方式仅在收集的数据集中有效，因此由此产生的偏置模型在现实世界的投入上表现不佳，或导致意外的社交影响，例如性别歧视。为了抵消偏差的影响，现有方法可以利用辅助信息，这在实践中很少可获得，或者在训练数据中的无偏见样本中筛选，希望能够充分存在清洁样品。但是，这些关于数据的推定并不总是保证。在本文中，我们提出了通过生成偏差变换〜（CDVG）对比下展，该〜（CDVG）能够在现有的方法中经营，其中现有方法由于未偏置的偏差样品而不足的预设而下降。通过我们的观察，不仅如前所述的鉴别模型，而且生成模型倾向于关注偏差，CDVG使用翻译模型来将样本中的偏置转换为另一种偏差模式，同时保留任务相关信息。 。通过对比学习，我们将转化的偏见视图与另一个学习偏见，学习偏见不变的表示。综合和现实世界数据集的实验结果表明，我们的框架优于目前的最先进，并且有效地阻止模型即使在无偏差样本极为稀缺时也会被偏置。

New architecture GPUs like A100 are now equipped with multi-instance GPU (MIG) technology, which allows the GPU to be partitioned into multiple small, isolated instances. This technology provides more flexibility for users to support both deep learning training and inference workloads, but efficiently utilizing it can still be challenging. The vision of this paper is to provide a more comprehensive and practical benchmark study for MIG in order to eliminate the need for tedious manual benchmarking and tuning efforts. To achieve this vision, the paper presents MIGPerf, an open-source tool that streamlines the benchmark study for MIG. Using MIGPerf, the authors conduct a series of experiments, including deep learning training and inference characterization on MIG, GPU sharing characterization, and framework compatibility with MIG. The results of these experiments provide new insights and guidance for users to effectively employ MIG, and lay the foundation for further research on the orchestration of hybrid training and inference workloads on MIGs. The code and results are released on https://github.com/MLSysOps/MIGProfiler. This work is still in progress and more results will be published soon.

In the scenario of black-box adversarial attack, the target model's parameters are unknown, and the attacker aims to find a successful adversarial perturbation based on query feedback under a query budget. Due to the limited feedback information, existing query-based black-box attack methods often require many queries for attacking each benign example. To reduce query cost, we propose to utilize the feedback information across historical attacks, dubbed example-level adversarial transferability. Specifically, by treating the attack on each benign example as one task, we develop a meta-learning framework by training a meta-generator to produce perturbations conditioned on benign examples. When attacking a new benign example, the meta generator can be quickly fine-tuned based on the feedback information of the new task as well as a few historical attacks to produce effective perturbations. Moreover, since the meta-train procedure consumes many queries to learn a generalizable generator, we utilize model-level adversarial transferability to train the meta-generator on a white-box surrogate model, then transfer it to help the attack against the target model. The proposed framework with the two types of adversarial transferability can be naturally combined with any off-the-shelf query-based attack methods to boost their performance, which is verified by extensive experiments.

We present X-Decoder, a generalized decoding model that can predict pixel-level segmentation and language tokens seamlessly. X-Decodert takes as input two types of queries: (i) generic non-semantic queries and (ii) semantic queries induced from text inputs, to decode different pixel-level and token-level outputs in the same semantic space. With such a novel design, X-Decoder is the first work that provides a unified way to support all types of image segmentation and a variety of vision-language (VL) tasks. Further, our design enables seamless interactions across tasks at different granularities and brings mutual benefits by learning a common and rich pixel-level visual-semantic understanding space, without any pseudo-labeling. After pretraining on a mixed set of a limited amount of segmentation data and millions of image-text pairs, X-Decoder exhibits strong transferability to a wide range of downstream tasks in both zero-shot and finetuning settings. Notably, it achieves (1) state-of-the-art results on open-vocabulary segmentation and referring segmentation on eight datasets; (2) better or competitive finetuned performance to other generalist and specialist models on segmentation and VL tasks; and (3) flexibility for efficient finetuning and novel task composition (e.g., referring captioning and image editing). Code, demo, video, and visualization are available at https://x-decoder-vl.github.io.

With increasing privacy concerns on data, recent studies have made significant progress using federated learning (FL) on privacy-sensitive natural language processing (NLP) tasks. Much literature suggests fully fine-tuning pre-trained language models (PLMs) in the FL paradigm can mitigate the data heterogeneity problem and close the performance gap with centralized training. However, large PLMs bring the curse of prohibitive communication overhead and local model adaptation costs for the FL system. To this end, we introduce various parameter-efficient tuning (PETuning) methods into federated learning. Specifically, we provide a holistic empirical study of representative PLMs tuning methods in FL. The experimental results cover the analysis of data heterogeneity levels, data scales, and different FL scenarios. Overall communication overhead can be significantly reduced by locally tuning and globally aggregating lightweight model parameters while maintaining acceptable performance in various FL settings. To facilitate the research of PETuning in FL, we also develop a federated tuning framework FedPETuning, which allows practitioners to exploit different PETuning methods under the FL training paradigm conveniently. The source code is available at \url{https://github.com/iezhuozhuo/FedETuning/tree/deltaTuning}.

Semi-supervised learning (SSL) has achieved great success in leveraging a large amount of unlabeled data to learn a promising classifier. A popular approach is pseudo-labeling that generates pseudo labels only for those unlabeled data with high-confidence predictions. As for the low-confidence ones, existing methods often simply discard them because these unreliable pseudo labels may mislead the model. Nevertheless, we highlight that these data with low-confidence pseudo labels can be still beneficial to the training process. Specifically, although the class with the highest probability in the prediction is unreliable, we can assume that this sample is very unlikely to belong to the classes with the lowest probabilities. In this way, these data can be also very informative if we can effectively exploit these complementary labels, i.e., the classes that a sample does not belong to. Inspired by this, we propose a novel Contrastive Complementary Labeling (CCL) method that constructs a large number of reliable negative pairs based on the complementary labels and adopts contrastive learning to make use of all the unlabeled data. Extensive experiments demonstrate that CCL significantly improves the performance on top of existing methods. More critically, our CCL is particularly effective under the label-scarce settings. For example, we yield an improvement of 2.43% over FixMatch on CIFAR-10 only with 40 labeled data.

Accurate spatial-temporal traffic flow forecasting is essential for helping traffic managers to take control measures and drivers to choose the optimal travel routes. Recently, graph convolutional networks (GCNs) have been widely used in traffic flow prediction owing to their powerful ability to capture spatial-temporal dependencies. The design of the spatial-temporal graph adjacency matrix is a key to the success of GCNs, and it is still an open question. This paper proposes reconstructing the binary adjacency matrix via tensor decomposition, and a traffic flow forecasting method is proposed. First, we reformulate the spatial-temporal fusion graph adjacency matrix into a three-way adjacency tensor. Then, we reconstructed the adjacency tensor via Tucker decomposition, wherein more informative and global spatial-temporal dependencies are encoded. Finally, a Spatial-temporal Synchronous Graph Convolutional module for localized spatial-temporal correlations learning and a Dilated Convolution module for global correlations learning are assembled to aggregate and learn the comprehensive spatial-temporal dependencies of the road network. Experimental results on four open-access datasets demonstrate that the proposed model outperforms state-of-the-art approaches in terms of the prediction performance and computational cost.

We introduce the MAsked Generative VIdeo Transformer, MAGVIT, to tackle various video synthesis tasks with a single model. We introduce a 3D tokenizer to quantize a video into spatial-temporal visual tokens and propose an embedding method for masked video token modeling to facilitate multi-task learning. We conduct extensive experiments to demonstrate the quality, efficiency, and flexibility of MAGVIT. Our experiments show that (i) MAGVIT performs favorably against state-of-the-art approaches and establishes the best-published FVD on three video generation benchmarks, including the challenging Kinetics-600. (ii) MAGVIT outperforms existing methods in inference time by two orders of magnitude against diffusion models and by 60x against autoregressive models. (iii) A single MAGVIT model supports ten diverse generation tasks and generalizes across videos from different visual domains. The source code and trained models will be released to the public at https://magvit.cs.cmu.edu.

Current methods for few-shot action recognition mainly fall into the metric learning framework following ProtoNet. However, they either ignore the effect of representative prototypes or fail to enhance the prototypes with multimodal information adequately. In this work, we propose a novel Multimodal Prototype-Enhanced Network (MORN) to use the semantic information of label texts as multimodal information to enhance prototypes, including two modality flows. A CLIP visual encoder is introduced in the visual flow, and visual prototypes are computed by the Temporal-Relational CrossTransformer (TRX) module. A frozen CLIP text encoder is introduced in the text flow, and a semantic-enhanced module is used to enhance text features. After inflating, text prototypes are obtained. The final multimodal prototypes are then computed by a multimodal prototype-enhanced module. Besides, there exist no evaluation metrics to evaluate the quality of prototypes. To the best of our knowledge, we are the first to propose a prototype evaluation metric called Prototype Similarity Difference (PRIDE), which is used to evaluate the performance of prototypes in discriminating different categories. We conduct extensive experiments on four popular datasets. MORN achieves state-of-the-art results on HMDB51, UCF101, Kinetics and SSv2. MORN also performs well on PRIDE, and we explore the correlation between PRIDE and accuracy.