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cognitive_synergy [2018/03/05 19:02]
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cognitive_synergy [2018/05/23 11:39] (current)
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 https://​arxiv.org/​pdf/​1802.10151v1.pdf Augmented CycleGAN: Learning Many-to-Many Mappings https://​arxiv.org/​pdf/​1802.10151v1.pdf Augmented CycleGAN: Learning Many-to-Many Mappings
 from Unpaired Data from Unpaired Data
 +
 +https://​arxiv.org/​abs/​1803.08495 Text2Shape: Generating Shapes from Natural Language by Learning Joint Embeddings
 +
 +We present a method for generating colored 3D shapes from natural language. To this end, we first learn joint embeddings of freeform text descriptions and colored 3D shapes. Our model combines and extends learning by association and metric learning approaches to learn implicit cross-modal connections,​ and produces a joint representation that captures the many-to-many relations between language and physical properties of 3D shapes such as color and shape. To evaluate our approach, we collect a large dataset of natural language descriptions for physical 3D objects in the ShapeNet dataset. With this learned joint embedding we demonstrate text-to-shape retrieval that outperforms baseline approaches. Using our embeddings with a novel conditional Wasserstein GAN framework, we generate colored 3D shapes from text. Our method is the first to connect natural language text with realistic 3D objects exhibiting rich variations in color, texture, and shape detail. ​  ​http://​text2shape.stanford.edu/​
 +
 +https://​arxiv.org/​pdf/​1804.00104v1.pdf Joint-VAE: Learning Disentangled Joint Continuous and Discrete Representations
 +
 +We have proposed Joint-VAE, a framework for learning disentangled continuous and discrete representations
 +in an unsupervised manner. The framework comes with the advantages of VAEs such
 +as stable training and large sample diversity while being able to model complex jointly continuous
 +and discrete generative factors. We have shown that Joint-VAE disentangles factors of variation on
 +several datasets while producing realistic samples. In addition, the inference network can be used to
 +infer unlabeled quantities on test data and to edit and manipulate images.
 +
 +https://​arxiv.org/​pdf/​1804.00410v1.pdf SyncGAN: Synchronize the Latent Space of Cross-modal
 +Generative Adversarial Networks
 +
 +. Instead of learning the transfer between different modalities, we aim to learn a synchronous latent space
 +representing the cross-modal common concept. A novel network component named synchronizer is proposed in this work to
 +judge whether the paired data is synchronous/​corresponding or not, which can constrain the latent space of generators in the
 +GANs. Our GAN model, named as SyncGAN, can successfully generate synchronous data (e.g., a pair of image and sound)
 +from identical random noise. For transforming data from one modality to another, we recover the latent code by inverting
 +the mappings of a generator and use it to generate data of different modality. In addition, the proposed model can achieve
 +semi-supervised learning, which makes our model more flexible for practical applications.
 +
 +Cross-domain GANs adopt several special mechanisms such as cycle-consistency and weight-sharing to extract the common
 +structure of cross-domain data automatically. However, the common structure does not exist between most cross-modal data
 +due to the heterogeneous gap. Therefore, the model need paired information to relate the different structures between data
 +of various modalities which are of the same concept.
 +
 +https://​arxiv.org/​abs/​1703.04368v1 Symbol Grounding via Chaining of Morphisms
 +
 +https://​arxiv.org/​abs/​1805.04174 Joint Embedding of Words and Labels for Text Classification
 +
 +Word embeddings are effective intermediate representations for capturing semantic regularities between words, when learning the representations of text sequences. We propose to view text classification as a label-word joint embedding problem: each label is embedded in the same space with the word vectors. We introduce an attention framework that measures the compatibility of embeddings between text sequences and labels. The attention is learned on a training set of labeled samples to ensure that, given a text sequence, the relevant words are weighted higher than the irrelevant ones. Our method maintains the interpretability of word embeddings, and enjoys a built-in ability to leverage alternative sources of information,​ in addition to input text sequences. Extensive results on the several large text datasets show that the proposed framework outperforms the state-of-the-art methods by a large margin, in terms of both accuracy and speed.
 +
 +https://​arxiv.org/​abs/​1805.08720 Adversarial Training of Word2Vec for Basket Completion
 +
 +In recent years, the Word2Vec model trained with the Negative Sampling loss function has shown state-of-the-art results in a number of machine learning tasks, including language modeling tasks, such as word analogy and word similarity, and in recommendation tasks, through Prod2Vec, an extension that applies to modeling user shopping activity and user preferences. Several methods that aim to improve upon the standard Negative Sampling loss have been proposed. In our paper we pursue more sophisticated Negative Sampling, by leveraging ideas from the field of Generative Adversarial Networks (GANs), and propose Adversarial Negative Sampling. We build upon the recent progress made in stabilizing the training objective of GANs in the discrete data setting, and introduce a new GAN-Word2Vec model.We evaluate our model on the task of basket completion, and show significant improvements in performance over Word2Vec trained using standard loss functions, including Noise Contrastive Estimation and Negative Sampling.