Deep multitask networks, in which one neural network produces multiple predictive outputs, can offer better speed and performance than their single-task counterparts but are challenging to train properly. We present a gradient normalization (GradNorm) algorithm that automatically balances training in deep multitask models by dynamically tuning gradient magnitudes. We show that for various network architectures, for both regression and classification tasks, and on both synthetic and real datasets, GradNorm improves accuracy and reduces overfitting across multiple tasks when compared to single-task networks, static baselines, and other adaptive multitask loss balancing techniques. GradNorm also matches or surpasses the performance of exhaustive grid search methods, despite only involving a single asymmetry hyperparameter α. Thus, what was once a tedious search process that incurred exponentially more compute for each task added can now be accomplished within a few training runs, irrespective of the number of tasks. Ultimately, we will demonstrate that gradient manipulation affords us great control over the training dynamics of multitask networks and may be one of the keys to unlocking the potential of multitask learning.

We propose a novel multi-task learning architecture, which allows learning of task-specific feature-level attention. Our design, the Multi-Task Attention Network (MTAN), consists of a single shared network containing a global feature pool, together with a soft-attention module for each task. These modules allow for learning of taskspecific features from the global features, whilst simultaneously allowing for features to be shared across different tasks. The architecture can be trained end-to-end and can be built upon any feed-forward neural network, is simple to implement, and is parameter efficient. We evaluate our approach on a variety of datasets, across both image-toimage predictions and image classification tasks. We show that our architecture is state-of-the-art in multi-task learning compared to existing methods, and is also less sensitive to various weighting schemes in the multi-task loss function. Code is available at https://github.com/ lorenmt/mtan.

Numerous deep learning applications benefit from multitask learning with multiple regression and classification objectives. In this paper we make the observation that the performance of such systems is strongly dependent on the relative weighting between each task's loss. Tuning these weights by hand is a difficult and expensive process, making multi-task learning prohibitive in practice. We propose a principled approach to multi-task deep learning which weighs multiple loss functions by considering the homoscedastic uncertainty of each task. This allows us to simultaneously learn various quantities with different units or scales in both classification and regression settings. We demonstrate our model learning per-pixel depth regression, semantic and instance segmentation from a monocular input image. Perhaps surprisingly, we show our model can learn multi-task weightings and outperform separate models trained individually on each task.

神经架构的创新促进了语言建模和计算机视觉中的重大突破。不幸的是，如果网络参数未正确初始化，新颖的架构通常会导致挑战超参数选择和培训不稳定。已经提出了许多架构特定的初始化方案，但这些方案并不总是可移植到新体系结构。本文介绍了毕业，一种用于初始化神经网络的自动化和架构不可知论由方法。毕业基础是一个简单的启发式;调整每个网络层的规范，使得具有规定的超参数的SGD或ADAM的单个步骤导致可能的损耗值最小。通过在每个参数块前面引入标量乘数变量，然后使用简单的数字方案优化这些变量来完成此调整。 GradInit加速了许多卷积架构的收敛性和测试性能，无论是否有跳过连接，甚至没有归一化层。它还提高了机器翻译的原始变压器架构的稳定性，使得在广泛的学习速率和动量系数下使用ADAM或SGD来训练它而无需学习速率预热。代码可在https://github.com/zhuchen03/gradinit上获得。

我们提出了一个统一的查看，即通过通用表示，一个深层神经网络共同学习多个视觉任务和视觉域。同时学习多个问题涉及最大程度地减少具有不同幅度和特征的多个损失函数的加权总和，从而导致一个损失的不平衡状态，与学习每个问题的单独模型相比，一个损失的不平衡状态主导了优化和差的结果。为此，我们提出了通过小容量适配器将多个任务/特定于域网络的知识提炼到单个深神经网络中的知识。我们严格地表明，通用表示在学习NYU-V2和CityScapes中多个密集的预测问题方面实现了最新的表现，来自视觉Decathlon数据集中的不同域中的多个图像分类问题以及MetadataSet中的跨域中的几个域中学习。最后，我们还通过消融和定性研究进行多次分析。

We present a novel and practical deep fully convolutional neural network architecture for semantic pixel-wise segmentation termed SegNet. This core trainable segmentation engine consists of an encoder network, a corresponding decoder network followed by a pixel-wise classification layer. The architecture of the encoder network is topologically identical to the 13 convolutional layers in the VGG16 network [1]. The role of the decoder network is to map the low resolution encoder feature maps to full input resolution feature maps for pixel-wise classification. The novelty of SegNet lies is in the manner in which the decoder upsamples its lower resolution input feature map(s). Specifically, the decoder uses pooling indices computed in the max-pooling step of the corresponding encoder to perform non-linear upsampling. This eliminates the need for learning to upsample. The upsampled maps are sparse and are then convolved with trainable filters to produce dense feature maps. We compare our proposed architecture with the widely adopted FCN [2] and also with the well known DeepLab-LargeFOV [3], DeconvNet [4] architectures. This comparison reveals the memory versus accuracy trade-off involved in achieving good segmentation performance. SegNet was primarily motivated by scene understanding applications. Hence, it is designed to be efficient both in terms of memory and computational time during inference. It is also significantly smaller in the number of trainable parameters than other competing architectures and can be trained end-to-end using stochastic gradient descent. We also performed a controlled benchmark of SegNet and other architectures on both road scenes and SUN RGB-D indoor scene segmentation tasks. These quantitative assessments show that SegNet provides good performance with competitive inference time and most efficient inference memory-wise as compared to other architectures. We also provide a Caffe implementation of SegNet and a web demo at http://mi.eng.cam.ac.uk/projects/segnet/.

Jitendra Malik once said, "Supervision is the opium of the AI researcher". Most deep learning techniques heavily rely on extreme amounts of human labels to work effectively. In today's world, the rate of data creation greatly surpasses the rate of data annotation. Full reliance on human annotations is just a temporary means to solve current closed problems in AI. In reality, only a tiny fraction of data is annotated. Annotation Efficient Learning (AEL) is a study of algorithms to train models effectively with fewer annotations. To thrive in AEL environments, we need deep learning techniques that rely less on manual annotations (e.g., image, bounding-box, and per-pixel labels), but learn useful information from unlabeled data. In this thesis, we explore five different techniques for handling AEL.

In this paper we address three different computer vision tasks using a single multiscale convolutional network architecture: depth prediction, surface normal estimation, and semantic labeling. The network that we develop is able to adapt naturally to each task using only small modifications, regressing from the input image to the output map directly. Our method progressively refines predictions using a sequence of scales, and captures many image details without any superpixels or low-level segmentation. We achieve state-of-the-art performance on benchmarks for all three tasks.

Due to object detection's close relationship with video analysis and image understanding, it has attracted much research attention in recent years. Traditional object detection methods are built on handcrafted features and shallow trainable architectures. Their performance easily stagnates by constructing complex ensembles which combine multiple low-level image features with high-level context from object detectors and scene classifiers. With the rapid development in deep learning, more powerful tools, which are able to learn semantic, high-level, deeper features, are introduced to address the problems existing in traditional architectures. These models behave differently in network architecture, training strategy and optimization function, etc. In this paper, we provide a review on deep learning based object detection frameworks. Our review begins with a brief introduction on the history of deep learning and its representative tool, namely Convolutional Neural Network (CNN). Then we focus on typical generic object detection architectures along with some modifications and useful tricks to improve detection performance further. As distinct specific detection tasks exhibit different characteristics, we also briefly survey several specific tasks, including salient object detection, face detection and pedestrian detection. Experimental analyses are also provided to compare various methods and draw some meaningful conclusions. Finally, several promising directions and tasks are provided to serve as guidelines for future work in both object detection and relevant neural network based learning systems.

生成的对抗网络（GANS）已经实现了图像生成的照片逼真品质。但是，如何最好地控制图像内容仍然是一个开放的挑战。我们介绍了莱特基照片，这是一个两级GaN，它在古典GAN目标上训练了训练，在一组空间关键点上有内部调节。这些关键点具有相关的外观嵌入，分别控制生成对象的位置和样式及其部件。我们使用合适的网络架构和培训方案地址的一个主要困难在没有领域知识和监督信号的情况下将图像解开到空间和外观因素中。我们展示了莱特基点提供可解释的潜在空间，可用于通过重新定位和交换Keypoint Embedding来重新安排生成的图像，例如通过组合来自不同图像的眼睛，鼻子和嘴巴来产生肖像。此外，关键点和匹配图像的显式生成启用了一种用于无监督的关键点检测的新的GaN的方法。

深神网络的对象探测器正在不断发展，并用于多种应用程序，每个应用程序都有自己的要求集。尽管关键安全应用需要高准确性和可靠性，但低延迟任务需要资源和节能网络。不断提出了实时探测器，在高影响现实世界中是必需的，但是它们过分强调了准确性和速度的提高，而其他功能（例如多功能性，鲁棒性，资源和能源效率）则被省略。现有网络的参考基准不存在，设计新网络的标准评估指南也不存在，从而导致比较模棱两可和不一致的比较。因此，我们对广泛的数据集进行了多个实时探测器（基于锚点，关键器和变压器）的全面研究，并报告了一系列广泛指标的结果。我们还研究了变量，例如图像大小，锚固尺寸，置信阈值和架构层对整体性能的影响。我们分析了检测网络的鲁棒性，以防止分配变化，自然腐败和对抗性攻击。此外，我们提供了校准分析来评估预测的可靠性。最后，为了强调现实世界的影响，我们对自动驾驶和医疗保健应用进行了两个独特的案例研究。为了进一步衡量关键实时应用程序中网络的能力，我们报告了在Edge设备上部署检测网络后的性能。我们广泛的实证研究可以作为工业界对现有网络做出明智选择的指南。我们还希望激发研究社区的设计和评估网络的新方向，该网络着重于更大而整体的概述，以实现深远的影响。

We introduce Bootstrap Your Own Latent (BYOL), a new approach to self-supervised image representation learning. BYOL relies on two neural networks, referred to as online and target networks, that interact and learn from each other. From an augmented view of an image, we train the online network to predict the target network representation of the same image under a different augmented view. At the same time, we update the target network with a slow-moving average of the online network. While state-of-the art methods rely on negative pairs, BYOL achieves a new state of the art without them. BYOL reaches 74.3% top-1 classification accuracy on ImageNet using a linear evaluation with a ResNet-50 architecture and 79.6% with a larger ResNet. We show that BYOL performs on par or better than the current state of the art on both transfer and semi-supervised benchmarks. Our implementation and pretrained models are given on GitHub. 3 * Equal contribution; the order of first authors was randomly selected.

转移学习可以在源任务上重新使用知识来帮助学习目标任务。一种简单的转移学习形式在当前的最先进的计算机视觉模型中是常见的，即预先训练ILSVRC数据集上的图像分类模型，然后在任何目标任务上进行微调。然而，先前对转移学习的系统研究已经有限，并且预计工作的情况并不完全明白。在本文中，我们对跨越不同的图像域进行了广泛的转移学习实验探索（消费者照片，自主驾驶，空中图像，水下，室内场景，合成，特写镜头）和任务类型（语义分割，物体检测，深度估计，关键点检测）。重要的是，这些都是与现代计算机视觉应用相关的复杂的结构化的输出任务类型。总共执行超过2000年的转移学习实验，包括许多来源和目标来自不同的图像域，任务类型或两者。我们系统地分析了这些实验，了解图像域，任务类型和数据集大小对传输学习性能的影响。我们的研究导致了几个见解和具体建议：（1）对于大多数任务，存在一个显着优于ILSVRC'12预培训的来源; （2）图像领域是实现阳性转移的最重要因素; （3）源数据集应该\ \ emph {include}目标数据集的图像域以获得最佳结果; （4）与此同时，当源任务的图像域比目标的图像域时，我们只观察小的负面影响; （5）跨任务类型的转移可能是有益的，但其成功严重依赖于源和目标任务类型。

Image classification with small datasets has been an active research area in the recent past. However, as research in this scope is still in its infancy, two key ingredients are missing for ensuring reliable and truthful progress: a systematic and extensive overview of the state of the art, and a common benchmark to allow for objective comparisons between published methods. This article addresses both issues. First, we systematically organize and connect past studies to consolidate a community that is currently fragmented and scattered. Second, we propose a common benchmark that allows for an objective comparison of approaches. It consists of five datasets spanning various domains (e.g., natural images, medical imagery, satellite data) and data types (RGB, grayscale, multispectral). We use this benchmark to re-evaluate the standard cross-entropy baseline and ten existing methods published between 2017 and 2021 at renowned venues. Surprisingly, we find that thorough hyper-parameter tuning on held-out validation data results in a highly competitive baseline and highlights a stunted growth of performance over the years. Indeed, only a single specialized method dating back to 2019 clearly wins our benchmark and outperforms the baseline classifier.

Given a grayscale photograph as input, this paper attacks the problem of hallucinating a plausible color version of the photograph. This problem is clearly underconstrained, so previous approaches have either relied on significant user interaction or resulted in desaturated colorizations. We propose a fully automatic approach that produces vibrant and realistic colorizations. We embrace the underlying uncertainty of the problem by posing it as a classification task and use class-rebalancing at training time to increase the diversity of colors in the result. The system is implemented as a feed-forward pass in a CNN at test time and is trained on over a million color images. We evaluate our algorithm using a "colorization Turing test," asking human participants to choose between a generated and ground truth color image. Our method successfully fools humans on 32% of the trials, significantly higher than previous methods. Moreover, we show that colorization can be a powerful pretext task for self-supervised feature learning, acting as a cross-channel encoder. This approach results in state-of-the-art performance on several feature learning benchmarks.

尽管深度强化学习（RL）最近取得了许多成功，但其方法仍然效率低下，这使得在数据方面解决了昂贵的许多问题。我们的目标是通过利用未标记的数据中的丰富监督信号来进行学习状态表示，以解决这一问题。本文介绍了三种不同的表示算法，可以访问传统RL算法使用的数据源的不同子集使用：（i）GRICA受到独立组件分析（ICA）的启发，并训练深层神经网络以输出统计独立的独立特征。输入。 Grica通过最大程度地减少每个功能与其他功能之间的相互信息来做到这一点。此外，格里卡仅需要未分类的环境状态。 （ii）潜在表示预测（LARP）还需要更多的上下文：除了要求状态作为输入外，它还需要先前的状态和连接它们的动作。该方法通过预测当前状态和行动的环境的下一个状态来学习状态表示。预测器与图形搜索算法一起使用。 （iii）重新培训通过训练深层神经网络来学习国家表示，以学习奖励功能的平滑版本。该表示形式用于预处理输入到深度RL，而奖励预测指标用于奖励成型。此方法仅需要环境中的状态奖励对学习表示表示。我们发现，每种方法都有其优势和缺点，并从我们的实验中得出结论，包括无监督的代表性学习在RL解决问题的管道中可以加快学习的速度。

最近的多任务学习研究旨在反对单一的标准化，其中培训只需最大限度地减少任务损失的总和。代替了几种Ad-hoc多任务优化算法，它受到各种假设的启发，关于使多任务设置困难的原因。这些优化器中的大多数都需要每个任务渐变，并引入重要的内存，运行时和实现开销。我们提出了一个理论分析，表明许多专业的多任务优化器可以被解释为正规化的形式。此外，我们表明，当与单任务学习的标准正则化和稳定技术耦合时，单一的标定化匹配或改善在监督和加固学习设置中复杂的多任务优化器的性能。我们相信我们的结果要求对该地区最近的研究进行关键重新评估。

我们介绍Softmax梯度篡改，一种用于修改神经网络后向通过的梯度的技术，以提高其准确性。我们的方法使用基于功率的概率变换来改变预测的概率值，然后将梯度重新计算在后向通过。这种修改导致更平滑的渐变简介，我们在经验和理论上展示。我们对剩余网络进行了转换参数进行了网格搜索。我们证明修改CUMMNET中的软MAX梯度可能导致培训准确性提高，从而增加训练数据的适合，并最大限度地利用神经网络的学习能力。当与标签平滑等正则化技术相结合时，我们获得更好的测试度量和更低的泛化间隙。 Softmax渐变篡改在ImageNet DataSet上的基线上以0.52 \％$ 0.52 \％$ 0.52 \％$ 0.52 \％。我们的方法非常通用，可以跨各种不同的网络架构和数据集使用。

Continual Learning (CL) is a field dedicated to devise algorithms able to achieve lifelong learning. Overcoming the knowledge disruption of previously acquired concepts, a drawback affecting deep learning models and that goes by the name of catastrophic forgetting, is a hard challenge. Currently, deep learning methods can attain impressive results when the data modeled does not undergo a considerable distributional shift in subsequent learning sessions, but whenever we expose such systems to this incremental setting, performance drop very quickly. Overcoming this limitation is fundamental as it would allow us to build truly intelligent systems showing stability and plasticity. Secondly, it would allow us to overcome the onerous limitation of retraining these architectures from scratch with the new updated data. In this thesis, we tackle the problem from multiple directions. In a first study, we show that in rehearsal-based techniques (systems that use memory buffer), the quantity of data stored in the rehearsal buffer is a more important factor over the quality of the data. Secondly, we propose one of the early works of incremental learning on ViTs architectures, comparing functional, weight and attention regularization approaches and propose effective novel a novel asymmetric loss. At the end we conclude with a study on pretraining and how it affects the performance in Continual Learning, raising some questions about the effective progression of the field. We then conclude with some future directions and closing remarks.

当代掌握检测方法采用深度学习，实现传感器和物体模型不确定性的鲁棒性。这两个主导的方法设计了掌握质量评分或基于锚的掌握识别网络。本文通过将其视为图像空间中的关键点检测来掌握掌握检测的不同方法。深网络检测每个掌握候选者作为一对关键点，可转换为掌握代表= {x，y，w，{\ theta}} t，而不是转角点的三态或四重奏。通过将关键点分组成对来降低检测难度提高性能。为了促进捕获关键点之间的依赖关系，将非本地模块结合到网络设计中。基于离散和连续定向预测的最终过滤策略消除了错误的对应关系，并进一步提高了掌握检测性能。此处提出的方法GKNET在康奈尔和伸缩的提花数据集上的精度和速度之间实现了良好的平衡（在41.67和23.26 fps的96.9％和98.39％）之间。操纵器上的后续实验使用4种类型的抓取实验来评估GKNet，反映不同滋扰的速度：静态抓握，动态抓握，在各种相机角度抓住，夹住。 GKNet优于静态和动态掌握实验中的参考基线，同时表现出变化的相机观点和中度杂波的稳健性。结果证实了掌握关键点是深度掌握网络的有效输出表示的假设，为预期的滋扰因素提供鲁棒性。