LAVAE: Disentangling Location and Appearance

We propose a probabilistic generative model for unsupervised learning of structured, interpretable, object-based representations of visual scenes. We use amortized variational inference to train the generative model end-to-end. The learned representations of object location and appearance are fully disentangled, and objects are represented independently of each other in the latent space. Unlike previous approaches that disentangle location and appearance, ours generalizes seamlessly to scenes with many more objects than encountered in the training regime. We evaluate the proposed model on multi-MNIST and multi-dSprites data sets

Optimal Variance Control of the Score Function Gradient Estimator for Importance Weighted Bounds

This paper introduces novel results for the score function gradient estimator of the importance weighted variational bound (IWAE). We prove that in the limit of large $K$ (number of importance samples) one can choose the control variate such that the Signal-to-Noise ratio (SNR) of the estimator grows as $\sqrt{K}$. This is in contrast to the standard pathwise gradient estimator where the SNR decreases as $1/\sqrt{K}$. Based on our theoretical findings we develop a novel control variate that extends on VIMCO. Empirically, for the training of both continuous and discrete generative models, the proposed method yields superior variance reduction, resulting in an SNR for IWAE that increases with $K$ without relying on the reparameterization trick. The novel estimator is competitive with state-of-the-art reparameterization-free gradient estimators such as Reweighted Wake-Sleep (RWS) and the thermodynamic variational objective (TVO) when training generative models

Semi-Supervised Variational Autoencoder for Survival Prediction

In this paper we propose a semi-supervised variational autoencoder for classification of overall survival groups from tumor segmentation masks. The model can use the output of any tumor segmentation algorithm, removing all assumptions on the scanning platform and the specific type of pulse sequences used, thereby increasing its generalization properties. Due to its semi-supervised nature, the method can learn to classify survival time by using a relatively small number of labeled subjects. We validate our model on the publicly available dataset from the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2019.Comment: Published in the pre-conference proceeding of "2019 International MICCAI BraTS Challenge

DiffEnc: Variational Diffusion with a Learned Encoder

Diffusion models may be viewed as hierarchical variational autoencoders (VAEs) with two improvements: parameter sharing for the conditional distributions in the generative process and efficient computation of the loss as independent terms over the hierarchy. We consider two changes to the diffusion model that retain these advantages while adding flexibility to the model. Firstly, we introduce a data- and depth-dependent mean function in the diffusion process, which leads to a modified diffusion loss. Our proposed framework, DiffEnc, achieves state-of-the-art likelihood on CIFAR-10. Secondly, we let the ratio of the noise variance of the reverse encoder process and the generative process be a free weight parameter rather than being fixed to 1. This leads to theoretical insights: For a finite depth hierarchy, the evidence lower bound (ELBO) can be used as an objective for a weighted diffusion loss approach and for optimizing the noise schedule specifically for inference. For the infinite-depth hierarchy, on the other hand, the weight parameter has to be 1 to have a well-defined ELBO

Generalization and Robustness Implications in Object-Centric Learning

The idea behind object-centric representation learning is that natural scenes can better be modeled as compositions of objects and their relations as opposed to distributed representations. This inductive bias can be injected into neural networks to potentially improve systematic generalization and learning efficiency of downstream tasks in scenes with multiple objects. In this paper, we train state-of-the-art unsupervised models on five common multi-object datasets and evaluate segmentation accuracy and downstream object property prediction. In addition, we study systematic generalization and robustness by investigating the settings where either single objects are out-of-distribution -- e.g., having unseen colors, textures, and shapes -- or global properties of the scene are altered -- e.g., by occlusions, cropping, or increasing the number of objects. From our experimental study, we find object-centric representations to be generally useful for downstream tasks and robust to shifts in the data distribution, especially if shifts affect single objects

On the Transfer of Disentangled Representations in Realistic Settings

Learning meaningful representations that disentangle the underlying structure of the data generating process is considered to be of key importance in machine learning. While disentangled representations were found to be useful for diverse tasks such as abstract reasoning and fair classification, their scalability and real-world impact remain questionable. We introduce a new high-resolution dataset with 1M simulated images and over 1,800 annotated real-world images of the same setup. In contrast to previous work, this new dataset exhibits correlations, a complex underlying structure, and allows to evaluate transfer to unseen simulated and real-world settings where the encoder i) remains in distribution or ii) is out of distribution. We propose new architectures in order to scale disentangled representation learning to realistic high-resolution settings and conduct a large-scale empirical study of disentangled representations on this dataset. We observe that disentanglement is a good predictor for out-of-distribution (OOD) task performance.Comment: Published at ICLR 202

Assaying Out-Of-Distribution Generalization in Transfer Learning

Since out-of-distribution generalization is a generally ill-posed problem, various proxy targets (e.g., calibration, adversarial robustness, algorithmic corruptions, invariance across shifts) were studied across different research programs resulting in different recommendations. While sharing the same aspirational goal, these approaches have never been tested under the same experimental conditions on real data. In this paper, we take a unified view of previous work, highlighting message discrepancies that we address empirically, and providing recommendations on how to measure the robustness of a model and how to improve it. To this end, we collect 172 publicly available dataset pairs for training and out-of-distribution evaluation of accuracy, calibration error, adversarial attacks, environment invariance, and synthetic corruptions. We fine-tune over 31k networks, from nine different architectures in the many- and few-shot setting. Our findings confirm that in- and out-of-distribution accuracies tend to increase jointly, but show that their relation is largely dataset-dependent, and in general more nuanced and more complex than posited by previous, smaller scale studies

Head-to-head comparison of plasma cTnI concentration values measured with three high-sensitivity methods in a large Italian population of healthy volunteers and patients admitted to emergency department with acute coronary syndrome: A multi-center study

Abstract Background The study aim is to compare cTnI values measured with three high-sensitivity (hs) methods in apparently healthy volunteers and patients admitted to emergency department (ED) with acute coronary syndrome enrolled in a large multicentre study. Methods Heparinized plasma samples were collected from 1511 apparently healthy subjects from 8 Italian clinical institutions (mean age: 51.5 years, SD: 14.1 years, range: 18–65 years, F/M ratio:0.95). All volunteers denied chronic or acute diseases and had normal values of routine laboratory tests. Moreover, 1322 heparinized plasma sample were also collected by 9 Italian clinical institutions from patients admitted to ED with clinical symptoms typical of acute coronary syndrome. The reference study laboratory assayed all plasma samples with three hs-methods: Architect hs-cTnI, Access hs-cTnI and ADVIA Centaur XPT methods. Principal Component Analysis (PCA) was also used to analyze the between-method differences among hs-cTnI assays. Results On average, a between-method difference of 31.2% CV was found among the results of hs-cTnI immunoassays. ADVIA Centaur XPT method measured higher cTnI values than Architect and Access methods. Moreover, 99th percentile URL values depended not only on age and sex of reference population, but also on the statistical approach used for calculation (robust non-parametric vs bootstrap). Conclusions Due to differences in concentrations and reference values, clinicians should be advised that plasma samples of the same patient should be measured for cTnI assay in the same laboratory. Specific clinical studies are needed to establish the most appropriate statistical approach to calculate the 99th percentile URL values for hs-cTnI methods

Evaluation of 99th percentile and reference change values of a high-sensitivity cTnI method: A multicenter study

Abstract Background The Italian Society of Clinical Biochemistry (SIBioC) and the Italian Section of the European Ligand Assay Society (ELAS) have recently promoted a multicenter study (Italian hs-cTnI Study) with the aim to accurately evaluate analytical performances and reference values of the most popular cTnI methods commercially available in Italy. The aim of this article is to report the results of the Italian hs-cTnI Study concerning the evaluation of the 99th percentile URL and reference change (RCV) values around the 99th URL of the Access cTnI method. Materials and methods Heparinized plasma samples were collected from 1306 healthy adult volunteers by 8 Italian clinical centers. Every center collected from 50 to 150 plasma samples from healthy adult subjects. All volunteers denied the presence of chronic or acute diseases and had normal values of routine laboratory tests (including creatinine, electrolytes, glucose and blood counts). An older cohort of 457 adult subjects (mean age 63.0 years; SD 8.1 years, minimum 47 years, maximum 86 years) underwent also ECG and cardiac imaging analysis in order to exclude the presence of asymptomatic cardiac disease. Results and conclusions The results of the present study confirm that the Access hsTnI method using the DxI platform satisfies the two criteria required by international guidelines for high-sensitivity methods for cTn assay. Furthermore, the results of this study confirm that the calculation of the 99th percentile URL values are greatly affected not only by age and sex of the reference population, but also by the statistical approach used for calculation of cTnI distribution parameters