基于GAN的生成建模的进展是，社区的推动是为了发现超出图像生成和编辑任务的使用。特别是，最近的几项工作表明，可以重新用诸如零件分割的判别任务重新用来重新用，尤其是当训练数据有限时。但这些改进如何解决自我监督学习的最新进展情况？由此引起这种激励，我们提出了一种基于对比学习的替代方法，并比较它们对标准的几次射击部分分割基准的性能。我们的实验表明，不仅GAN的方法不提供显着的性能优势，它们的多步训练很复杂，几乎是数量级较慢，并且可以引入额外的偏差。这些实验表明，由使用对比学习训练的标准前馈网络捕获的生成模型的感应偏差，例如它们的解开形状和纹理的能力。这些实验表明，目前生成模型中存在的电感偏差，例如它们的解开形状和纹理的能力，通过使用对比学习训练的标准前馈网络充分捕获。

The success of state-of-the-art deep neural networks heavily relies on the presence of large-scale labelled datasets, which are extremely expensive and time-consuming to annotate. This paper focuses on tackling semi-supervised part segmentation tasks by generating high-quality images with a pre-trained GAN and labelling the generated images with an automatic annotator. In particular, we formulate the annotator learning as a learning-to-learn problem. Given a pre-trained GAN, the annotator learns to label object parts in a set of randomly generated images such that a part segmentation model trained on these synthetic images with their predicted labels obtains low segmentation error on a small validation set of manually labelled images. We further reduce this nested-loop optimization problem to a simple gradient matching problem and efficiently solve it with an iterative algorithm. We show that our method can learn annotators from a broad range of labelled images including real images, generated images, and even analytically rendered images. Our method is evaluated with semi-supervised part segmentation tasks and significantly outperforms other semi-supervised competitors when the amount of labelled examples is extremely limited.

带有像素天标签的注释图像是耗时和昂贵的过程。最近，DataSetGan展示了有希望的替代方案 - 通过利用一小组手动标记的GaN生成的图像来通过生成的对抗网络（GAN）来综合大型标记数据集。在这里，我们将DataSetGan缩放到ImageNet类别的规模。我们从ImageNet上训练的类条件生成模型中拍摄图像样本，并为所有1K类手动注释每个类的5张图像。通过在Biggan之上培训有效的特征分割架构，我们将Bigan转换为标记的DataSet生成器。我们进一步表明，VQGan可以类似地用作数据集生成器，利用已经注释的数据。我们通过在各种设置中标记一组8K实图像并在各种设置中评估分段性能来创建一个新的想象因基准。通过广泛的消融研究，我们展示了利用大型生成的数据集来培训在像素 - 明智的任务上培训不同的监督和自我监督的骨干模型的大增益。此外，我们证明，使用我们的合成数据集进行预培训，以改善在几个下游数据集上的标准Imagenet预培训，例如Pascal-VOC，MS-Coco，Citycapes和Chink X射线以及任务（检测，细分）。我们的基准将公开并维护一个具有挑战性的任务的排行榜。项目页面：https://nv-tlabs.github.io/big-dataseTgan/

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.

去噪扩散概率模型最近获得了很多研究的关注，因为它们优于GAN，以及目前提供最先进的生成性能。扩散模型的卓越性能使它们在若干应用中为它们提供了吸引人的工具，包括尿素，超分辨率和语义编辑。在本文中，我们证明扩散模型也可以用作语义分割的仪器，特别是当标记数据稀缺时的设置中。特别地，对于几种预训练的扩散模型，我们研究了从执行反向扩散过程的马尔可夫步骤的网络的中间激活。我们表明这些激活有效地捕获了来自输入图像的语义信息，并且看起来是分割问题的优异像素级表示。基于这些观察，我们描述了一种简单的分段方法，即使仅提供了几种训练图像也可以工作。我们的方法显着优于若干数据集的现有替代品，以获得相同数量的人类监督。

监管基于深度学习的方法，产生医学图像分割的准确结果。但是，它们需要大量标记的数据集，并获得它们是一种艰苦的任务，需要临床专业知识。基于半/自我监督的学习方法通​​过利用未标记的数据以及有限的注释数据来解决此限制。最近的自我监督学习方法使用对比损失来从未标记的图像中学习良好的全球层面表示，并在像想象网那样的流行自然图像数据集上实现高性能。在诸如分段的像素级预测任务中，对于学习良好的本地级别表示以及全局表示来说至关重要，以实现更好的准确性。然而，现有的局部对比损失的方法的影响仍然是学习良好本地表现的限制，因为类似于随机增强和空间接近定义了类似和不同的局部区域;由于半/自我监督设置缺乏大规模专家注释，而不是基于当地地区的语义标签。在本文中，我们提出了局部对比损失，以便通过利用从未标记的图像的未标记图像的伪标签获得的语义标签信息来学习用于分割的良好像素级别特征。特别地，我们定义了建议的损失，以鼓励具有相同伪标签/标签的像素的类似表示，同时与数据集中的不同伪标签/标签的像素的表示。我们通过联合优化标记和未标记的集合和仅限于标记集的分割损失，通过联合优化拟议的对比损失来进行基于伪标签的自培训和培训网络。我们在三个公共心脏和前列腺数据集上进行了评估，并获得高分割性能。

Recently, unsupervised learning has made impressive progress on various tasks. Despite the dominance of discriminative models, increasing attention is drawn to representations learned by generative models and in particular, Generative Adversarial Networks (GANs). Previous works on the interpretation of GANs reveal that GANs encode semantics in feature maps in a linearly separable form. In this work, we further find that GAN's features can be well clustered with the linear separability assumption. We propose a novel clustering algorithm, named KLiSH, which leverages the linear separability to cluster GAN's features. KLiSH succeeds in extracting fine-grained semantics of GANs trained on datasets of various objects, e.g., car, portrait, animals, and so on. With KLiSH, we can sample images from GANs along with their segmentation masks and synthesize paired image-segmentation datasets. Using the synthesized datasets, we enable two downstream applications. First, we train semantic segmentation networks on these datasets and test them on real images, realizing unsupervised semantic segmentation. Second, we train image-to-image translation networks on the synthesized datasets, enabling semantic-conditional image synthesis without human annotations.

Recent work leverages the expressive power of generative adversarial networks (GANs) to generate labeled synthetic datasets. These dataset generation methods often require new annotations of synthetic images, which forces practitioners to seek out annotators, curate a set of synthetic images, and ensure the quality of generated labels. We introduce the HandsOff framework, a technique capable of producing an unlimited number of synthetic images and corresponding labels after being trained on less than 50 pre-existing labeled images. Our framework avoids the practical drawbacks of prior work by unifying the field of GAN inversion with dataset generation. We generate datasets with rich pixel-wise labels in multiple challenging domains such as faces, cars, full-body human poses, and urban driving scenes. Our method achieves state-of-the-art performance in semantic segmentation, keypoint detection, and depth estimation compared to prior dataset generation approaches and transfer learning baselines. We additionally showcase its ability to address broad challenges in model development which stem from fixed, hand-annotated datasets, such as the long-tail problem in semantic segmentation.

最近在图像编辑中找到了生成的对抗网络（GANS）。但是，大多数基于GaN的图像编辑方法通常需要具有用于训练的语义分段注释的大规模数据集，只提供高级控制，或者仅在不同图像之间插入。在这里，我们提出了EditGan，一种用于高质量，高精度语义图像编辑的新方法，允许用户通过修改高度详细的部分分割面罩，例如，为汽车前灯绘制新掩模来编辑图像。编辑登上的GAN框架上建立联合模型图像及其语义分割，只需要少数标记的示例，使其成为编辑的可扩展工具。具体地，我们将图像嵌入GaN潜在空间中，并根据分割编辑执行条件潜代码优化，这有效地修改了图像。算优化优化，我们发现在实现编辑的潜在空间中找到编辑向量。该框架允许我们学习任意数量的编辑向量，然后可以直接应用于交互式速率的其他图像。我们通过实验表明，EditGan可以用前所未有的细节和自由来操纵图像，同时保留完整的图像质量。我们还可以轻松地组合多个编辑并执行超出EditGan训练数据的合理编辑。我们在各种图像类型上展示编辑，并定量优于标准编辑基准任务的几种先前编辑方法。

In recent years, generative adversarial networks (GANs) have been an actively studied topic and shown to successfully produce high-quality realistic images in various domains. The controllable synthesis ability of GAN generators suggests that they maintain informative, disentangled, and explainable image representations, but leveraging and transferring their representations to downstream tasks is largely unexplored. In this paper, we propose to distill knowledge from GAN generators by squeezing and spanning their representations. We squeeze the generator features into representations that are invariant to semantic-preserving transformations through a network before they are distilled into the student network. We span the distilled representation of the synthetic domain to the real domain by also using real training data to remedy the mode collapse of GANs and boost the student network performance in a real domain. Experiments justify the efficacy of our method and reveal its great significance in self-supervised representation learning. Code is available at https://github.com/yangyu12/squeeze-and-span.

Current state-of-the-art segmentation techniques for ocular images are critically dependent on large-scale annotated datasets, which are labor-intensive to gather and often raise privacy concerns. In this paper, we present a novel framework, called BiOcularGAN, capable of generating synthetic large-scale datasets of photorealistic (visible light and near-infrared) ocular images, together with corresponding segmentation labels to address these issues. At its core, the framework relies on a novel Dual-Branch StyleGAN2 (DB-StyleGAN2) model that facilitates bimodal image generation, and a Semantic Mask Generator (SMG) component that produces semantic annotations by exploiting latent features of the DB-StyleGAN2 model. We evaluate BiOcularGAN through extensive experiments across five diverse ocular datasets and analyze the effects of bimodal data generation on image quality and the produced annotations. Our experimental results show that BiOcularGAN is able to produce high-quality matching bimodal images and annotations (with minimal manual intervention) that can be used to train highly competitive (deep) segmentation models (in a privacy aware-manner) that perform well across multiple real-world datasets. The source code for the BiOcularGAN framework is publicly available at https://github.com/dariant/BiOcularGAN.

几乎没有零件分割的目的是仅给出几个带注释的样本，将对象的不同部分分开。由于数据有限的挑战，现有的作品主要集中在学习分类器上，而不是预先训练的功能，无法学习针对零件细分的任务特定功能。在本文中，我们建议在“预训练” - “微调”范式中学习特定于任务的功能。我们进行及时设计以减少预训练任务（即图像生成）与下游任务（即部分分段）之间的差距，以便可以利用生成的GAN先验进行分割。这是通过将零件分割图投影到RGB空间中并在RGB分割图和原始图像之间进行插值来实现的。具体而言，我们设计了一种微调策略，以逐步将图像发生器调整到分割生成器中，在该机构中，生成器的监督通过插值从图像到分割图各不等。此外，我们提出了一个两流体系结构，即一个分割流以生成特定于任务的特征，以及一个图像流以提供空间约束。图像流可以视为自我监管的自动编码器，这使我们的模型能够从大规模的支持图像中受益。总体而言，这项工作是试图通过及时设计来探索一代任务和感知任务之间的内部相关性。广泛的实验表明，我们的模型可以在几个部分分割数据集上实现最新性能。

我们建议在2D域中利用自我监督的技术来实现细粒度的3D形状分割任务。这是受到观察的启发：基于视图的表面表示比基于点云或体素占用率的3D对应物更有效地建模高分辨率表面细节和纹理。具体而言，给定3D形状，我们将其从多个视图中渲染，并在对比度学习框架内建立密集的对应学习任务。结果，与仅在2D或3D中使用自学的替代方案相比，学到的2D表示是视图不变和几何一致的，在对有限的标记形状进行培训时，可以更好地概括概括。对纹理（渲染peple）和未纹理（partnet）3D数据集的实验表明，我们的方法在细粒部分分割中优于最先进的替代方案。当仅一组稀疏的视图可供训练或形状纹理时，对基准的改进就会更大，这表明MVDecor受益于2D处理和3D几何推理。

近年来有条件的GAN已经成熟，并且能够产生高质量的现实形象。但是，计算资源和培训高质量的GAN所需的培训数据是巨大的，因此对这些模型的转移学习的研究是一个紧急话题。在本文中，我们探讨了从高质量预训练的无条件GAN到有条件的GAN的转移。为此，我们提出了基于HyperNetwork的自适应权重调制。此外，我们介绍了一个自我初始化过程，不需要任何真实数据才能初始化HyperNetwork参数。为了进一步提高知识转移的样本效率，我们建议使用自我监督（对比）损失来改善GaN判别者。在广泛的实验中，我们验证了多个标准基准上的Hypernetworks，自我初始化和对比损失的效率。

The neural radiance field (NeRF) has shown promising results in preserving the fine details of objects and scenes. However, unlike mesh-based representations, it remains an open problem to build dense correspondences across different NeRFs of the same category, which is essential in many downstream tasks. The main difficulties of this problem lie in the implicit nature of NeRF and the lack of ground-truth correspondence annotations. In this paper, we show it is possible to bypass these challenges by leveraging the rich semantics and structural priors encapsulated in a pre-trained NeRF-based GAN. Specifically, we exploit such priors from three aspects, namely 1) a dual deformation field that takes latent codes as global structural indicators, 2) a learning objective that regards generator features as geometric-aware local descriptors, and 3) a source of infinite object-specific NeRF samples. Our experiments demonstrate that such priors lead to 3D dense correspondence that is accurate, smooth, and robust. We also show that established dense correspondence across NeRFs can effectively enable many NeRF-based downstream applications such as texture transfer.

在培训深层网络中进行部分分割的重要瓶颈是获得详细注释的成本。我们提出了一个框架，以利用粗糙标签，例如图形地面蒙版和关键点位置，这些位置容易用于某些类别以改善零件分割模型。一个关键的挑战是，这些注释是针对不同任务和不同的标签样式收集的，并且不能轻易地映射到零件标签上。为此，我们建议共同学习标签样式与部分分割模型之间的依赖关系，从而使我们能够利用来自不同标签的监督。为了评估我们的方法，我们在Caltech-UCSD鸟类和OID飞机数据集上开发了基准。我们的方法优于基于多任务学习，半监督学习和竞争方法的基准，这些方法依赖于手动设计的损失功能，以利用稀疏的supervision。

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。

尽管最近通过剩余网络的代表学习中的自我监督方法取得了进展，但它们仍然对ImageNet分类基准进行了高度的监督学习，限制了它们在性能关键设置中的适用性。在MITROVIC等人的现有理论上洞察中建立2021年，我们提出了RELICV2，其结合了明确的不变性损失，在各种适当构造的数据视图上具有对比的目标。 Relicv2在ImageNet上实现了77.1％的前1个分类准确性，使用线性评估使用Reset50架构和80.6％，具有较大的Reset型号，优于宽边缘以前的最先进的自我监督方法。最值得注意的是，RelicV2是使用一系列标准Reset架构始终如一地始终优先于类似的对比较中的监督基线的第一个表示学习方法。最后，我们表明，尽管使用Reset编码器，Relicv2可与最先进的自我监控视觉变压器相媲美。

从单个图像中的新视图综合最近实现了显着的结果，尽管在训练时需要某种形式的3D，姿势或多视图监管限制了实际情况的部署。这项工作旨在放松这些假设，可实现新颖的观看综合的条件生成模型，以完全无人监测。我们首先使用3D感知GaN制定预先列车纯粹的生成解码器模型，同时训练编码器网络将映射从潜空间颠覆到图像。然后，我们将编码器和解码器交换，并将网络作为条件GaN培训，其混合物类似于自动化器的物镜和自蒸馏。在测试时间，给定对象的视图，我们的模型首先将图像内容嵌入到潜在代码中并通过保留代码固定并改变姿势来生成它的新颖视图。我们在ShapeNet等合成数据集上测试我们的框架，如ShapeNet和无约束的自然图像集合，在那里没有竞争方法可以训练。

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.