Differences

This shows you the differences between two versions of the page.

Link to this comparison view

Both sides previous revision Previous revision
Next revision
Previous revision
Next revision Both sides next revision
cognitive_synergy [2018/02/04 11:17]
admin
cognitive_synergy [2018/04/19 18:24]
admin
Line 176: Line 176:
 https://​arxiv.org/​pdf/​1802.00273v1.pdf Emerging Language Spaces Learned From https://​arxiv.org/​pdf/​1802.00273v1.pdf Emerging Language Spaces Learned From
 Massively Multilingual Corpora Massively Multilingual Corpora
 +
 +https://​arxiv.org/​abs/​1803.00385 MAGAN: Aligning Biological Manifolds
 +
 + We present a new GAN called the Manifold-Aligning GAN (MAGAN) that aligns two manifolds such that related points in each measurement space are aligned together. We demonstrate applications of MAGAN in single-cell biology in integrating two different measurement types together. In our demonstrated examples, cells from the same tissue are measured with both genomic (single-cell RNA-sequencing) and proteomic (mass cytometry) technologies. We show that the MAGAN successfully aligns them such that known correlations between measured markers are improved compared to other recently proposed models.
 +
 +https://​arxiv.org/​pdf/​1802.10151v1.pdf Augmented CycleGAN: Learning Many-to-Many Mappings
 +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
 +