We investigate the problem of training generative models on a very sparse collection of 3D models. We use geometrically motivated energies to augment and thus boost a sparse collection of example (training) models. We analyze the Hessian of the as-rigid-as-possible (ARAP) energy to sample from and project to the underlying (local) shape space, and use the augmented dataset to train a variational autoencoder (VAE). We iterate the process of building latent spaces of VAE and augmenting the associated dataset, to progressively reveal a richer and more expressive generative space for creating geometrically and semantically valid samples. Our framework allows us to train generative 3D models even with a small set of good quality 3D models, which are typically hard to curate. We extensively evaluate our method against a set of strong baselines, provide ablation studies and demonstrate application towards establishing shape correspondences. We present multiple examples of interesting and meaningful shape variations even when starting from as few as 3-10 training shapes.
In this work, we address the challenging task of long-tailed image recognition. Previous long-tailed recognition methods commonly focus on the data augmentation or re-balancing strategy of the tail classes to give more attention to tail classes during the model training. However, due to the limited training images for tail classes, the diversity of tail class images is still restricted, which results in poor feature representations. In this work, we hypothesize that common latent features among the head and tail classes can be used to give better feature representation. Motivated by this, we i…
VINNA for Neonates -- Orientation Independence through Latent Augmentations
German Center for Neurodegenerative Diseases (DZNE)（德国神经退行性疾病中心（DZNE））；A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital（麻省总医院A.A.马蒂诺斯生物医学成像中心）；Harvard Medical School（哈佛医学院）
本文提出 VINNA 框架，通过将四自由度刚体变换模块嵌入网络架构实现分辨率感知的内部增强，在无需图像或标签插值的情况下有效解决新生儿脑 MRI 分割中的头部位置变化和多分辨率问题，并显著优于现有外部增强方法。
Max Planck Institute for Intelligent Systems Tübingen（马克斯·普朗克智能系统研究所（蒂宾根））；University of Cambridge（剑桥大学）；Tübingen AI Center, University of Tübingen（蒂宾根大学蒂宾根人工智能中心）；IMPRS for Intelligent Systems（智能系统国际马克斯·普朗克研究学院）；Amazon（亚马逊）
Self-supervised representation learning has shown remarkable success in a number of domains. A common practice is to perform data augmentation via hand-crafted transformations intended to leave the semantics of the data invariant. We seek to understand the empirical success of this approach from a theoretical perspective. We formulate the augmentation process as a latent variable model by postulating a partition of the latent representation into a content component, which is assumed invariant to augmentation, and a style component, which is allowed to change. Unlike prior work on disentangleme…
Generalization Performance of Deep Learning models trained using Empirical Risk Minimization can be improved significantly by using Data Augmentation strategies such as simple transformations, or using Mixed Samples. We attempt to empirically analyze the impact of such strategies on the transfer of generalization between teacher and student models in a distillation setup. We observe that if a teacher is trained using any of the mixed sample augmentation strategies, such as MixUp or CutMix, the student model distilled from it is impaired in its generalization capabilities. We hypothesize that s…
Adaptive Augmentation-Aware Latent Learning for Robust LiDAR Semantic Segmentation
University of Science and Technology of China（中国科学技术大学）；National Key Laboratory of Deep Space Exploration, Deep Space Exploration Laboratory（深空探测全国重点实验室（深空探测实验室））
Nanyang Technological University（南洋理工大学）；Singapore Management University（新加坡管理大学）；Salesforce Research Asia（赛富时亚洲研究院）
This paper investigates an open research problem of generating text-image pairs to improve the training of fine-grained image-to-text cross-modal retrieval task, and proposes a novel framework for paired data augmentation by uncovering the hidden semantic information of StyleGAN2 model. Specifically, we first train a StyleGAN2 model on the given dataset. We then project the real images back to the latent space of StyleGAN2 to obtain the latent codes. To make the generated images manipulatable, we further introduce a latent space alignment module to learn the alignment between StyleGAN2 latent …
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
We propose a composable framework for latent space image augmentation that allows for easy combination of multiple augmentations. Image augmentation has been shown to be an effective technique for improving the performance of a wide variety of image classification and generation tasks. Our framework is based on the Variational Autoencoder architecture and uses a novel approach for augmentation via linear transformation within the latent space itself. We explore losses and augmentation latent geometry to enforce the transformations to be composable and involuntary, thus allowing the transformat…
This paper proposes rollable latent space (RLS) for an azimuth invariant synthetic aperture radar (SAR) target recognition. Scarce labeled data and limited viewing direction are critical issues in SAR target recognition.The RLS is a designed space in which rolling of latent features corresponds to 3D rotation of an object. Thus latent features of an arbitrary view can be inferred using those of different views. This characteristic further enables us to augment data from limited viewing in RLS. RLS-based classifiers with and without data augmentation and a conventional classifier trained with t…
Imperial College London（帝国理工学院）；Technical University of Munich（慕尼黑工业大学）；Klinikum rechts der Isar（伊萨尔河右岸医院）；University of Edinburgh（爱丁堡大学）
Deep learning-based segmentation methods are vulnerable to unforeseen data distribution shifts during deployment, e.g. change of image appearances or contrasts caused by different scanners, unexpected imaging artifacts etc. In this paper, we present a cooperative framework for training image segmentation models and a latent space augmentation method for generating hard examples. Both contributions improve model generalization and robustness with limited data. The cooperative training framework consists of a fast-thinking network (FTN) and a slow-thinking network (STN). The FTN learns decoupled…
Transcending Forgery Specificity with Latent Space Augmentation for Generalizable Deepfake Detection
The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen)（香港中文大学（深圳））；University at Buffalo, State University of New York（纽约州立大学布法罗分校）；Nanjing University of Information Science and Technology（南京信息工程大学）
Imperial College London（帝国理工学院）；Middlesex University London（伦敦密德萨斯大学）
The rapid progress in machine learning methods has been empowered by i) huge datasets that have been collected and annotated, ii) improved engineering (e.g. data pre-processing/normalization). The existing datasets typically include several million samples, which constitutes their extension a colossal task. In addition, the state-of-the-art data-driven methods demand a vast amount of data, hence a standard engineering trick employed is artificial data augmentation for instance by adding into the data cropped and (affinely) transformed images. However, this approach does not correspond to any c…
Enhancing Face Recognition with Latent Space Data Augmentation and Facial Posture Reconstruction
School of Computer Engineering, Iran University of Science and Technology（伊朗科技大学计算机工程学院）；Technical and Engineering Faculty, South Tehran Branch, Islamic Azad University（伊斯兰阿扎德大学南德黑兰分校技术与工程学院）；Faculty of Engineering, University of Birjand（比尔詹德大学工程学院）；Faculty of Computer Engineering & Information Technology, Shahrood University of Technology（沙赫鲁德工业大学计算机工程与信息技术学院）
Binary classification (BC) is a practical task that is ubiquitous in real-world problems, such as distinguishing healthy and unhealthy objects in biomedical diagnostics and defective and non-defective products in manufacturing inspections. Nonetheless, fully annotated data are commonly required to effectively solve this problem, and their collection by domain experts is a tedious and expensive procedure. In contrast to BC, several significant semi-supervised learning techniques that heavily rely on stochastic data augmentation techniques have been devised for solving multi-class classification…
University of Toronto（多伦多大学）；Vector Institute（向量学院）；Google Research（谷歌研究院）
We introduce Exemplar VAEs, a family of generative models that bridge the gap between parametric and non-parametric, exemplar based generative models. Exemplar VAE is a variant of VAE with a non-parametric prior in the latent space based on a Parzen window estimator. To sample from it, one first draws a random exemplar from a training set, then stochastically transforms that exemplar into a latent code and a new observation. We propose retrieval augmented training (RAT) as a way to speed up Exemplar VAE training by using approximate nearest neighbor search in the latent space to define a lower…
Diffusion-Based Data Augmentation for Medical Image Segmentation
School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；Dept. of Engineering for Innovation Medicine, University of Verona（威尼斯大学创新医学工程系）
East China Normal University（华东师范大学）；Tencent Youtu Lab（腾讯优图实验室）；Shanghai Jiao Tong University（上海交通大学）；Zhejiang Lab（之江实验室）
Novelty detection is the process of determining whether a query example differs from the learned training distribution. Previous methods attempt to learn the representation of the normal samples via generative adversarial networks (GANs). However, they will suffer from instability training, mode dropping, and low discriminative ability. Recently, various pretext tasks (e.g. rotation prediction and clustering) have been proposed for self-supervised learning in novelty detection. However, the learned latent features are still low discriminative. We overcome such problems by introducing a novel d…
University of Science and Technology of China（中国科学技术大学）；JD Explore Academy（京东探索研究院）；The University of Sydney（悉尼大学）
Data-Efficient GANs (DE-GANs), which aim to learn generative models with a limited amount of training data, encounter several challenges for generating high-quality samples. Since data augmentation strategies have largely alleviated the training instability, how to further improve the generative performance of DE-GANs becomes a hotspot. Recently, contrastive learning has shown the great potential of increasing the synthesis quality of DE-GANs, yet related principles are not well explored. In this paper, we revisit and compare different contrastive learning strategies in DE-GANs, and identify (…
Meta AI；Inria（法国国家信息与自动化研究所）；Univ. Rennes（雷恩大学）；CNRS（法国国家科学研究中心）；IRISA（雷恩高等信息科学与应用数学研究院）
We revisit watermarking techniques based on pre-trained deep networks, in the light of self-supervised approaches. We present a way to embed both marks and binary messages into their latent spaces, leveraging data augmentation at marking time. Our method can operate at any resolution and creates watermarks robust to a broad range of transformations (rotations, crops, JPEG, contrast, etc). It significantly outperforms the previous zero-bit methods, and its performance on multi-bit watermarking is on par with state-of-the-art encoder-decoder architectures trained end-to-end for watermarking. The…
Medical image segmentation is an important task for computer aided diagnosis. Pixelwise manual annotations of large datasets require high expertise and is time consuming. Conventional data augmentations have limited benefit by not fully representing the underlying distribution of the training set, thus affecting model robustness when tested on images captured from different sources. Prior work leverages synthetic images for data augmentation ignoring the interleaved geometric relationship between different anatomical labels. We propose improvements over previous GAN-based medical image synthes…
