Representation learning is concerned with training machine learning algorithms to learn useful representations, e.g. those that are interpretable, have latent features, or can be used for transfer learning.
( Image credit: Visualizing and Understanding Convolutional Networks )
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Since real-world objects and their interactions are often multi-modal and multi-typed, heterogeneous networks have been widely used as a more powerful, realistic, and generic superclass of traditional homogeneous networks (graphs).
Specifically, we first propose an object-extent learning module for localizing the object according to the visual patterns shared among the instances in the same category.
Recently, with the increasing amount of affinity data available and the success of deep representation learning models on various domains, deep learning techniques have been applied to DTA prediction.
In this work, we investigate the use of Approximate Convex Decompositions (ACD) as a self-supervisory signal for label-efficient learning of point cloud representations.
In this study we show that in-order to extract high-quality context-sensitive node representations it is not needed to rely on supplementary node features, nor to employ computationally heavy and complex models.
Based on this hypothesis, we propose to learn point cloud representation by bidirectional reasoning between the local structures at different abstraction hierarchies and the global shape without human supervision.
Graph representation learning is a fundamental task of various applications, aiming to learn low-dimensional embeddings for nodes which can preserve graph topology information.
Current autonomous driving systems are composed of a perception system and a decision system.
Representation learning approaches require a massive amount of discriminative training data, which is unavailable in many scenarios, such as healthcare, small city, education, etc.
Recently, the emergence of pre-trained models (PTMs) has brought natural language processing (NLP) to a new era.