We present a framework for efficient perceptual inference that explicitly reasons about the segmentation of its inputs and features. Rather than being trained for any specific segmentation, our framework learns the grouping process in an unsupervised manner or alongside any supervised task. By enriching the representations of a neural network, we enable it to group the representations of different objects in an iterative manner. By allowing the system to amortize the iterative inference of the groupings, we achieve very fast convergence. In contrast to many other recently proposed methods for addressing multi-object scenes, our system does not assume the inputs to be images and can therefore directly handle other modalities. For multi-digit classification of very cluttered images that require texture segmentation, our method offers improved classification performance over convolutional networks despite being fully connected. Furthermore, we observe that our system greatly improves on the semi-supervised result of a baseline Ladder network on our dataset, indicating that segmentation can also improve sample efficiency.
Another closely related line of research @cite_12 @cite_22 has focused on multi-causal modeling of the inputs. Many of the works in that area @cite_5 @cite_4 @cite_14 @cite_30 build upon Restricted Boltzmann Machines. Each input is modeled as a mixture model with a separate latent variable for each object. Because exact inference is intractable, these models approximate the posterior with some form of expectation maximization @cite_32 or sampling procedure. Our assumptions are very similar to these approaches, but we allow the model to learn the amortized inference directly (more in line with ).
@cite_30 @cite_14 @cite_4 @cite_22 @cite_32 @cite_5 @cite_12
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We present a generative model of images based on layering, in which image layers are individually generated, then composited from front to back. We are thus able to factor the appearance of an image into the appearance of individual objects within the image --- and additionally for each individual object, we can factor content from pose. Unlike prior work on layered models, we learn a shape prior for each object layer, allowing the model to tease out which object is in front by looking for a consistent shape, without needing access to motion cues or any labeled data. We show that ordinary stochastic gradient variational bayes (SGVB), which optimizes our fully differentiable lower-bound on the log-likelihood, is sufficient to learn an interpretable representation of images. Finally we present experiments demonstrating the effectiveness of the model for inferring foreground and background objects in images. Unsupervised feature learning has emerged as a promising tool in learning representations from unlabeled data. However, it is still challenging to learn useful high-level features when the data contains a significant amount of irrelevant patterns. Although feature selection can be used for such complex data, it may fail when we have to build a learning system from scratch (i.e., starting from the lack of useful raw features). To address this problem, we propose a point-wise gated Boltzmann machine, a unified generative model that combines feature learning and feature selection. Our model performs not only feature selection on learned high-level features (i.e., hidden units), but also dynamic feature selection on raw features (i.e., visible units) through a gating mechanism. For each example, the model can adaptively focus on a variable subset of visible nodes corresponding to the task-relevant patterns, while ignoring the visible units corresponding to the task-irrelevant patterns. In experiments, our method achieves improved performance over state-of-the-art in several visual recognition benchmarks. While Boltzmann Machines have been successful at unsupervised learning and density modeling of images and speech data, they can be very sensitive to noise in the data. In this paper, we introduce a novel model, the Robust Boltzmann Machine (RoBM), which allows Boltzmann Machines to be robust to corruptions. In the domain of visual recognition, the RoBM is able to accurately deal with occlusions and noise by using multiplicative gating to induce a scale mixture of Gaussians over pixels. Image denoising and in-painting correspond to posterior inference in the RoBM. Our model is trained in an unsupervised fashion with unlabeled noisy data and can learn the spatial structure of the occluders. Compared to standard algorithms, the RoBM is significantly better at recognition and denoising on several face databases. Many perceptual models and theories hinge on treating objects as a collection of constituent parts. When applying these approaches to data, a fundamental problem arises: how can we determine what are the parts? We attack this problem using learning, proposing a form of generative latent factor model, in which each data dimension is allowed to select a different factor or part as its explanation. This approach permits a range of variations that posit different models for the appearance of a part. Here we provide the details for two such models: a discrete and a continuous one. Further, we show that this latent factor model can be extended hierarchically to account for correlations between the appearances of different parts. This permits modelling of data consisting of multiple categories, and learning these categories simultaneously with the parts when they are unobserved. Experiments demonstrate the ability to learn parts-based representations, and categories, of facial images and user-preference data. Computer vision has grown tremendously in the past two decades. Despite all efforts, existing attempts at matching parts of the human visual system's extraordinary ability to understand visual scenes lack either scope or power. By combining the advantages of general low-level generative models and powerful layer-based and hierarchical models, this work aims at being a first step toward richer, more flexible models of images. After comparing various types of restricted Boltzmann machines (RBMs) able to model continuous-valued data, we introduce our basic model, the masked RBM, which explicitly models occlusion boundaries in image patches by factoring the appearance of any patch region from its shape. We then propose a generative model of larger images using a field of such RBMs. Finally, we discuss how masked RBMs could be stacked to form a deep model able to generate more complicated structures and suitable for various tasks such as segmentation or object recognition. This paper presents a formulation for unsupervised learning of clusters reflecting multiple causal structure in binary data. Unlike the "hard" k-means clustering algorithm and the "soft" mixture model, each of which assumes that a single hidden event generates each data point, a multiple cause model accounts for observed data by combining assertions from many hidden causes, each of which can pertain to varying degree to any subset of the observable dimensions. We employ an objective function and iterative gradient descent learning algorithm resembling the conventional mixture model. A crucial issue is the mixing function for combining beliefs from different cluster centers in order to generate data predictions whose errors are minimized both during recognition and learning. The mixing function constitutes a prior assumption about underlying structural regularities of the data domain; we demonstrate a weakness inherent to the popular weighted sum followed by sigmoid squashing, and offer alternative forms of the nonlinearity for two types of data domain. Results are presented demonstrating the algorithm's ability successfully to discover coherent multiple causal representations in several experimental data sets.