6,554 research outputs found
Robust Multimodal Representation Learning with Evolutionary Adversarial Attention Networks
Multimodal representation learning is beneficial for many multimedia-oriented applications such as social image recognition and visual question answering. The different modalities of the same instance (e.g., a social image and its corresponding description) are usually correlational and complementary. Most existing approaches for multimodal representation learning are not effective to model the deep correlation between different modalities. Moreover, it is difficult for these approaches to deal with the noise within social images. In this paper, we propose a deep learning-based approach named Evolutionary Adversarial Attention Networks (EAAN), which combines the attention mechanism with adversarial networks through evolutionary training, for robust multimodal representation learning. Specifically, a two-branch visual-textual attention model is proposed to correlate visual and textual content for joint representation. Then adversarial networks are employed to impose regularization upon the representation by matching its posterior distribution to the given priors. Finally, the attention model and adversarial networks are integrated into an evolutionary training framework for robust multimodal representation learning. Extensive experiments have been conducted on four real-world datasets, including PASCAL, MIR, CLEF, and NUS-WIDE. Substantial performance improvements on the tasks of image classification and tag recommendation demonstrate the superiority of the proposed approach
Learning Multimodal Graph-to-Graph Translation for Molecular Optimization
We view molecular optimization as a graph-to-graph translation problem. The
goal is to learn to map from one molecular graph to another with better
properties based on an available corpus of paired molecules. Since molecules
can be optimized in different ways, there are multiple viable translations for
each input graph. A key challenge is therefore to model diverse translation
outputs. Our primary contributions include a junction tree encoder-decoder for
learning diverse graph translations along with a novel adversarial training
method for aligning distributions of molecules. Diverse output distributions in
our model are explicitly realized by low-dimensional latent vectors that
modulate the translation process. We evaluate our model on multiple molecular
optimization tasks and show that our model outperforms previous
state-of-the-art baselines
Generative Adversarial Text to Image Synthesis
Automatic synthesis of realistic images from text would be interesting and
useful, but current AI systems are still far from this goal. However, in recent
years generic and powerful recurrent neural network architectures have been
developed to learn discriminative text feature representations. Meanwhile, deep
convolutional generative adversarial networks (GANs) have begun to generate
highly compelling images of specific categories, such as faces, album covers,
and room interiors. In this work, we develop a novel deep architecture and GAN
formulation to effectively bridge these advances in text and image model- ing,
translating visual concepts from characters to pixels. We demonstrate the
capability of our model to generate plausible images of birds and flowers from
detailed text descriptions.Comment: ICML 201
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