Deep learning-based fault diagnosis of photovoltaic systems: A comprehensive review and enhancement prospects
Photovoltaic (PV) systems are subject to failures during their operation due to the aging
effects and external/environmental conditions. These faults may affect the different system …
effects and external/environmental conditions. These faults may affect the different system …
Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling
We study the problem of 3D object generation. We propose a novel framework, namely 3D
Generative Adversarial Network (3D-GAN), which generates 3D objects from a probabilistic …
Generative Adversarial Network (3D-GAN), which generates 3D objects from a probabilistic …
A papier-mâché approach to learning 3d surface generation
We introduce a method for learning to generate the surface of 3D shapes. Our approach
represents a 3D shape as a collection of parametric surface elements and, in contrast to …
represents a 3D shape as a collection of parametric surface elements and, in contrast to …
Vse++: Improving visual-semantic embeddings with hard negatives
We present a new technique for learning visual-semantic embeddings for cross-modal
retrieval. Inspired by hard negative mining, the use of hard negatives in structured …
retrieval. Inspired by hard negative mining, the use of hard negatives in structured …
Deep metric learning via lifted structured feature embedding
Learning the distance metric between pairs of examples is of great importance for learning
and visual recognition. With the remarkable success from the state of the art convolutional …
and visual recognition. With the remarkable success from the state of the art convolutional …
Abo: Dataset and benchmarks for real-world 3d object understanding
Abstract We introduce Amazon Berkeley Objects (ABO), a new large-scale dataset designed
to help bridge the gap between real and virtual 3D worlds. ABO contains product catalog …
to help bridge the gap between real and virtual 3D worlds. ABO contains product catalog …
Learning a predictable and generative vector representation for objects
What is a good vector representation of an object? We believe that it should be generative in
3D, in the sense that it can produce new 3D objects; as well as be predictable from 2D, in …
3D, in the sense that it can produce new 3D objects; as well as be predictable from 2D, in …
Deep metric learning with angular loss
The modern image search system requires semantic understanding of image, and a key yet
under-addressed problem is to learn a good metric for measuring the similarity between …
under-addressed problem is to learn a good metric for measuring the similarity between …
Render for cnn: Viewpoint estimation in images using cnns trained with rendered 3d model views
Object viewpoint estimation from 2D images is an essential task in computer vision.
However, two issues hinder its progress: scarcity of training data with viewpoint annotations …
However, two issues hinder its progress: scarcity of training data with viewpoint annotations …
What do single-view 3d reconstruction networks learn?
Convolutional networks for single-view object reconstruction have shown impressive
performance and have become a popular subject of research. All existing techniques are …
performance and have become a popular subject of research. All existing techniques are …