Search Results for author:
Found 8 papers, 5 papers with code
Neural Mesh Fusion: Unsupervised 3D Planar Surface Understanding
This paper presents Neural Mesh Fusion (NMF), an efficient approach for joint optimization of polygon mesh from multi-view image observations and unsupervised 3D planar-surface parsing of the scene.
Hyperbolic Vision Transformers: Combining Improvements in Metric Learning
Following this line of work, we propose a new hyperbolic-based model for metric learning.
Ranked #1 on
Metric Learning
on CUB-200-2011
Latent Transformations via NeuralODEs for GAN-based Image Editing
Recent advances in high-fidelity semantic image editing heavily rely on the presumably disentangled latent spaces of the state-of-the-art generative models, such as StyleGAN.
Disentangled Representations from Non-Disentangled Models
Constructing disentangled representations is known to be a difficult task, especially in the unsupervised scenario.
Tensorized Embedding Layers
The embedding layers transforming input words into real vectors are the key components of deep neural networks used in natural language processing.
Performance of Hyperbolic Geometry Models on Top-N Recommendation Tasks
We introduce a simple autoencoder based on hyperbolic geometry for solving standard collaborative filtering problem.
Hyperbolic Image Embeddings
Computer vision tasks such as image classification, image retrieval and few-shot learning are currently dominated by Euclidean and spherical embeddings, so that the final decisions about class belongings or the degree of similarity are made using linear hyperplanes, Euclidean distances, or spherical geodesic distances (cosine similarity).
Tensorized Embedding Layers for Efficient Model Compression
The embedding layers transforming input words into real vectors are the key components of deep neural networks used in natural language processing.
