This paper presents SimCLR: a simple framework for contrastive learning of visual representations. We simplify recently proposed contrastive self-supervised learning algorithms without requiring specialized architectures or a memory bank. In order to understand what enables the contrastive prediction tasks to learn useful representations, we systematically study the major components of our framework. We show that (1) composition of data augmentations plays a critical role in defining effective predictive tasks, (2) introducing a learnable nonlinear transformation between the representation and the contrastive loss substantially improves the quality of the learned representations, and (3) contrastive learning benefits from larger batch sizes and more training steps compared to supervised learning. By combining these findings, we are able to considerably outperform previous methods for self-supervised and semi-supervised learning on ImageNet. A linear classifier trained on self-supervised representations learned by SimCLR achieves 76.5% top-1 accuracy, which is a 7% relative improvement over previous state-of-the-art, matching the performance of a supervised ResNet-50. When fine-tuned on only 1% of the labels, we achieve 85.8% top-5 accuracy, outperforming AlexNet with 100X fewer labels.

pdf/a-simple-framework-for-contrastive-learning-of-visual-1xcr7l89og.pdf

A Simple Framework for Contrastive Learning of Visual Representations

A survey on deep learning in medical image analysis

Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available. In this paper, we systematically study model scaling and identify that carefully balancing network depth, width, and resolution can lead to better performance. Based on this observation, we propose a new scaling method that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient. We demonstrate the effectiveness of this method on scaling up MobileNets and ResNet. 
To go even further, we use neural architecture search to design a new baseline network and scale it up to obtain a family of models, called EfficientNets, which achieve much better accuracy and efficiency than previous ConvNets. In particular, our EfficientNet-B7 achieves state-of-the-art 84.3% top-1 accuracy on ImageNet, while being 8.4x smaller and 6.1x faster on inference than the best existing ConvNet. Our EfficientNets also transfer well and achieve state-of-the-art accuracy on CIFAR-100 (91.7%), Flowers (98.8%), and 3 other transfer learning datasets, with an order of magnitude fewer parameters. Source code is at this https URL.

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

/pdf/efficientnet-rethinking-model-scaling-for-convolutional-4sqngzi1zt.pdf

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.

Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning

Automatic face recognition in unconstrained conditions is a difficult task which has recently attained increasing attention. In this domain, face verification methods have significantly improved since the release of the Labeled Faces in the Wild database, but the related problem of face identification, is still lacking considerations, which is partly because of the shortage of representative databases. Only recently, two new datasets called Remote Face and Point-and-Shoot Challenge were published providing appropriate benchmarks for the research community to investigate the problem of face recognition in challenging imaging conditions, in both, verification and identification modes. In this paper we provide an in-depth examination of three local binary description methods in unconstrained face recognition evaluating them on these two recently published datasets. In detail, we investigate three well established methods separately and fusing them at rank- and score-levels. We are using a well-defined evaluation protocol allowing a fair comparison of our results for future examinations.

An In-depth Examination of Local Binary Descriptors in Unconstrained Face Recognition

We present a joint prior that takes intensity and depth information into account. The prior is defined using a flexible Field-of-Experts model and is learned from a database of natural images. It is a generative model and has an efficient method for sampling. We use sampling from the model to perform in painting and up sampling of depth maps when intensity information is available. We show that including the intensity information in the prior improves the results obtained from the model. We also compare to another two-channel inpainting approach and show superior results.

/pdf/a-learned-joint-depth-and-intensity-prior-using-markov-148ropf1dx.pdf

A Learned Joint Depth and Intensity Prior Using Markov Random Fields

Combining long sequences of overlapping depth maps without simplification results in a huge number of redundant points, which slows down further processing. In this paper, a novel method is presented for incrementally creating a nonredundant point cloud with varying levels of detail without limiting the captured volume or requiring any parameters from the user. Overlapping measurements are used to refine point estimates by reducing their directional variance. The algorithm was evaluated with plane and cube fitting residuals, which were improved considerably over redundant point clouds.

/pdf/merging-overlapping-depth-maps-into-a-nonredundant-point-fctv4vc5j5.pdf

Merging Overlapping Depth Maps into a Nonredundant Point Cloud

This paper presents a probabilistic approach for object localization which combines subspace clustering with the selection of discriminative clusters. Clustering is often a key step in object recognition and is penalized by the high dimensionality of the descriptors. Indeed, local descriptors, such as SIFT, which have shown excellent results in recognition, are high-dimensional and live in different low-dimensional subspaces. We therefore use a subspace clustering method called High-Dimensional Data Clustering (HDDC) which overcomes the curse of dimensionality. Furthermore, in many cases only a few of the clusters are useful to discriminate the object. We, thus, evaluate the discriminative capacity of clusters and use it to compute the probability that a local descriptor belongs to the object. Experimental results demonstrate the effectiveness of our probabilistic approach for object localization and show that subspace clustering gives better results compared to standard clustering methods. Furthermore, our approach outperforms existing results for the Pascal 2005 dataset.

/pdf/object-localization-by-subspace-clustering-of-local-1xcbcpwzmj.pdf

Object localization by subspace clustering of local descriptors

We present a generative autoencoder that provides fast encoding, faithful reconstructions (eg. retaining the identity of a face), sharp generated/reconstructed samples in high resolutions, and a well-structured latent space that supports semantic manipulation of the inputs. There are no current autoencoder or GAN models that satisfactorily achieve all of these. We build on the progressively growing autoencoder model PIONEER, for which we completely alter the training dynamics based on a careful analysis of recently introduced normalization schemes. We show significantly improved visual and quantitative results for face identity conservation in CelebAHQ. Our model achieves state-of-the-art disentanglement of latent space, both quantitatively and via realistic image attribute manipulations. On the LSUN Bedrooms dataset, we improve the disentanglement performance of the vanilla PIONEER, despite having a simpler model. Overall, our results indicate that the PIONEER networks provide a way towards photorealistic face manipulation.

Towards Photographic Image Manipulation with Balanced Growing of Generative Autoencoders

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