现有类增量学习（CIL）方法基于对数据标签敏感的监督分类框架。根据新类数据更新它们时，它们会遭受灾难性忘记：该模型无法清楚地从新的旧数据数据中清晰地辨别旧类数据。在本文中，我们第一次探讨了类增量学习（SSCIL）中自我监督表示学习的性能，丢弃了数据标签和模型的分类器。为了全面讨论CIL中监督和自我监督方法之间的性能差异，我们设置了三种不同的类增量方案：随机类方案，语义类方案和群集方案，以模拟各种类增量学习方案。此外，我们提出了线性评估协议（LEP）和泛化评估协议（GEP），以降低模型的代表性分类能力和CIL泛化。我们的实验（在ImageNet-100和Imagenet上）表明SSCIL具有比CIL中的监督策略更好的反忘记能力和鲁棒性。要了解什么，可以缓解SSCIL中的灾难性遗忘，我们研究SSCIL的主要组成部分并得出结论（1）不同数据增强的组成提高了模型的表示的质量，\ extent {GrayScale}操作会降低数据的系统噪声在SSCIL中增强。 （2）投影仪（如缓冲区）降低了SSCIL中模型的不必要参数更新，并增加了模型的稳健性。虽然SSCIL的性能明显高于CIL中的监督方法，但仍然有一个明显的差距，具有联合学习。我们的探索为大规模数据集提供了自我监督课程增量学习的基线，有助于减轻CIL灾难遗忘的一些前进策略。

虽然灾难性遗忘的概念是直截了当的，但缺乏对其原因的研究。在本文中，我们系统地探索并揭示了课堂增量学习中灾难性遗忘的三个原因（CIL）。从代表学习的角度来看，（i）当学习者未能正确对准相同相位数据时，逐步忘记在训练所得和（ii）当学习者混淆当前相数据时发生相互相互混淆上一阶段。从特定于任务特定的角度来看，CIL模型遭受了（iii）分类器偏差的问题。在调查现有策略后，我们观察到缺乏关于如何防止相互局部混淆的研究。要启动对该具体问题的研究，我们提出了一种简单但有效的框架，CIL（C4IL）的对比阶级浓度。我们的框架利用了对比度学习的阶级集中效应，产生了具有更好的级别的紧凑性和阶级间可分离的表示分布。经验上，我们观察到C4IL显着降低了相互相连的概率，并且结果提高了多个数据集的多个CIL设置的性能。

We study the composition style in deep image matting, a notion that characterizes a data generation flow on how to exploit limited foregrounds and random backgrounds to form a training dataset. Prior art executes this flow in a completely random manner by simply going through the foreground pool or by optionally combining two foregrounds before foreground-background composition. In this work, we first show that naive foreground combination can be problematic and therefore derive an alternative formulation to reasonably combine foregrounds. Our second contribution is an observation that matting performance can benefit from a certain occurrence frequency of combined foregrounds and their associated source foregrounds during training. Inspired by this, we introduce a novel composition style that binds the source and combined foregrounds in a definite triplet. In addition, we also find that different orders of foreground combination lead to different foreground patterns, which further inspires a quadruplet-based composition style. Results under controlled experiments on four matting baselines show that our composition styles outperform existing ones and invite consistent performance improvement on both composited and real-world datasets. Code is available at: https://github.com/coconuthust/composition_styles

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.

Script is a kind of structured knowledge extracted from texts, which contains a sequence of events. Based on such knowledge, script event prediction aims to predict the subsequent event. To do so, two aspects should be considered for events, namely, event description (i.e., what the events should contain) and event encoding (i.e., how they should be encoded). Most existing methods describe an event by a verb together with only a few core arguments (i.e., subject, object, and indirect object), which are not precise. In addition, existing event encoders are limited to a fixed number of arguments, which are not flexible to deal with extra information. Thus, in this paper, we propose the Rich Event Prediction (REP) framework for script event prediction. Fundamentally, it is based on the proposed rich event description, which enriches the existing ones with three kinds of important information, namely, the senses of verbs, extra semantic roles, and types of participants. REP contains an event extractor to extract such information from texts. Based on the extracted rich information, a predictor then selects the most probable subsequent event. The core component of the predictor is a transformer-based event encoder to flexibly deal with an arbitrary number of arguments. Experimental results on the widely used Gigaword Corpus show the effectiveness of the proposed framework.

A hallmark of the deep learning era for computer vision is the successful use of large-scale labeled datasets to train feature representations for tasks ranging from object recognition and semantic segmentation to optical flow estimation and novel view synthesis of 3D scenes. In this work, we aim to learn dense discriminative object representations for low-shot category recognition without requiring any category labels. To this end, we propose Deep Object Patch Encodings (DOPE), which can be trained from multiple views of object instances without any category or semantic object part labels. To train DOPE, we assume access to sparse depths, foreground masks and known cameras, to obtain pixel-level correspondences between views of an object, and use this to formulate a self-supervised learning task to learn discriminative object patches. We find that DOPE can directly be used for low-shot classification of novel categories using local-part matching, and is competitive with and outperforms supervised and self-supervised learning baselines. Code and data available at https://github.com/rehg-lab/dope_selfsup.

Continual learning (CL) learns a sequence of tasks incrementally. There are two popular CL settings, class incremental learning (CIL) and task incremental learning (TIL). A major challenge of CL is catastrophic forgetting (CF). While a number of techniques are already available to effectively overcome CF for TIL, CIL remains to be highly challenging. So far, little theoretical study has been done to provide a principled guidance on how to solve the CIL problem. This paper performs such a study. It first shows that probabilistically, the CIL problem can be decomposed into two sub-problems: Within-task Prediction (WP) and Task-id Prediction (TP). It further proves that TP is correlated with out-of-distribution (OOD) detection, which connects CIL and OOD detection. The key conclusion of this study is that regardless of whether WP and TP or OOD detection are defined explicitly or implicitly by a CIL algorithm, good WP and good TP or OOD detection are necessary and sufficient for good CIL performances. Additionally, TIL is simply WP. Based on the theoretical result, new CIL methods are also designed, which outperform strong baselines in both CIL and TIL settings by a large margin.

我们将点隶属关系引入特征Upsmpling，这一概念描述了每个上采样点的隶属关系到具有语义相似性的本地解码器特征点形成的语义群集。通过重新思考点的隶属关系，我们提出了一种通用公式，用于产生上采样内核。内核不仅鼓励语义平滑度，还鼓励上采样的特征图中的边界清晰度。此类属性对于某些密集的预测任务（例如语义分割）特别有用。我们公式的关键思想是通过比较每个编码器特征点与解码器特征的空间相关局部区域之间的相似性来生成相似性感知的内核。通过这种方式，编码器特征点可以作为提示，以告知UPS采样特征点的语义集群。为了体现该配方，我们进一步实例化了轻巧的增加采样算子，称为相似性 - 吸引点隶属关系（SAPA），并研究其变体。 SAPA会在许多密集的预测任务上邀请一致的性能改进，包括语义分割，对象检测，深度估计和图像垫。代码可用：https：//github.com/poppinace/sapa

在强化学习中，蒙特卡洛算法通过平均偶发回报来更新Q功能。在Monte Carlo UCB（MC-UCB）算法中，在每个状态下采取的动作是最大化Q函数加上UCB勘探项的动作，该术语偏向于选择频率较低的动作的选择。尽管在为MC-UCB建立遗憾界限方面已经进行了重要的工作，但大多数工作都集中在该问题的有限培训版本上，每个情节都在不断数量的步骤后终止。对于此类有限的Horizo​​n问题，最佳策略既取决于当前状态和情节中的时间。但是，对于许多自然的情节问题，例如GO，CHESS和机器人任务等游戏，该情节是随机的，最佳政策是静止的。对于此类环境，MC-UCB中的Q功能是否会收敛到最佳Q函数，这是一个空旷的问题。我们猜想，与Q学习不同，它并不是所有MDP的收敛。尽管如此，我们表明，对于大型MDP，其中包括二十一点和确定性MDP等随机MDP，例如GO，MC-UCB中的Q功能几乎可以肯定地收敛到最佳Q函数。该结果的直接推论是，它几乎肯定会为所有有限的Horizo​​n MDP收敛。我们还提供了数值实验，为MC-UCB提供了进一步的见解。

本文研究持续学习（CL）的逐步学习（CIL）。已经提出了许多方法来处理CIL中的灾难性遗忘（CF）。大多数方法都会为单个头网络中所有任务的所有类别构建单个分类器。为了防止CF，一种流行的方法是记住以前任务中的少数样本，并在培训新任务时重播它们。但是，这种方法仍然患有严重的CF，因为在内存中仅使用有限的保存样本数量来更新或调整了先前任务的参数。本文提出了一种完全不同的方法，该方法使用变压器网络为每个任务（称为多头模型）构建一个单独的分类器（头部），称为更多。与其在内存中使用保存的样本在现有方法中更新以前的任务/类的网络，不如利用保存的样本来构建特定任务分类器（添加新的分类头），而无需更新用于先前任务/类的网络。新任务的模型经过培训，可以学习任务的类别，并且还可以检测到不是从相同数据分布（即，均分布（OOD））的样本。这使测试实例属于的任务的分类器能够为正确的类产生高分，而其他任务的分类器可以产生低分，因为测试实例不是来自这些分类器的数据分布。实验结果表明，更多的表现优于最先进的基线，并且自然能够在持续学习环境中进行OOD检测。