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Contrastive Learning Using Spectral Methods
In many natural settings, the analysis goal is not to characterize a single data set in isolation, but rather to understand the difference between one set of observations and another. For example, given a background corpus of news articles together with writings of a particular author, one may want a topic model that explains word patterns and themes specific to the author. Another example comes from genomics, in which biological signals may be collected from different regions of a genome, and one wants a model that captures the differential statistics observed in these regions. This paper formalizes this notion of contrastive learning for mixture models, and develops spectral algorithms for inferring mixture components specific to a foreground data set when contrasted with a background data set. The method builds on recent moment-based estimators and tensor decompositions for latent variable models, and has the intuitive feature of using background data statistics to appropriately modify moments estimated from foreground data. A key advantage of the method is that the background data need only be coarsely modeled, which is important when the background is too complex, noisy, or not of interest. The method is demonstrated on applications in contrastive topic modeling and genomic sequence analysis.Engineering and Applied Science
Spectral Normalized Dual Contrastive Regularization for Image-to-Image Translation
Existing image-to-image (I2I) translation methods achieve state-of-the-art
performance by incorporating the patch-wise contrastive learning into
Generative Adversarial Networks. However, patch-wise contrastive learning only
focuses on the local content similarity but neglects the global structure
constraint, which affects the quality of the generated images. In this paper,
we propose a new unpaired I2I translation framework based on dual contrastive
regularization and spectral normalization, namely SN-DCR. To maintain
consistency of the global structure and texture, we design the dual contrastive
regularization using different deep feature spaces respectively. In order to
improve the global structure information of the generated images, we formulate
a semantically contrastive loss to make the global semantic structure of the
generated images similar to the real images from the target domain in the
semantic feature space. We use Gram Matrices to extract the style of texture
from images. Similarly, we design style contrastive loss to improve the global
texture information of the generated images. Moreover, to enhance the stability
of model, we employ the spectral normalized convolutional network in the design
of our generator. We conduct the comprehensive experiments to evaluate the
effectiveness of SN-DCR, and the results prove that our method achieves SOTA in
multiple tasks
Conditional Restricted Boltzmann Machines for Structured Output Prediction
Conditional Restricted Boltzmann Machines (CRBMs) are rich probabilistic
models that have recently been applied to a wide range of problems, including
collaborative filtering, classification, and modeling motion capture data.
While much progress has been made in training non-conditional RBMs, these
algorithms are not applicable to conditional models and there has been almost
no work on training and generating predictions from conditional RBMs for
structured output problems. We first argue that standard Contrastive
Divergence-based learning may not be suitable for training CRBMs. We then
identify two distinct types of structured output prediction problems and
propose an improved learning algorithm for each. The first problem type is one
where the output space has arbitrary structure but the set of likely output
configurations is relatively small, such as in multi-label classification. The
second problem is one where the output space is arbitrarily structured but
where the output space variability is much greater, such as in image denoising
or pixel labeling. We show that the new learning algorithms can work much
better than Contrastive Divergence on both types of problems
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