Search Results for author:
Found 7 papers, 6 papers with code
On Self-Supervised Image Representations for GAN Evaluation
The embeddings from CNNs pretrained on Imagenet classification are de-facto standard image representations for assessing GANs via FID, Precision and Recall measures.
Object Segmentation Without Labels with Large-Scale Generative Models
The recent rise of unsupervised and self-supervised learning has dramatically reduced the dependency on labeled data, providing effective image representations for transfer to downstream vision tasks.
Beyond Vector Spaces: Compact Data Representation as Differentiable Weighted Graphs
Our main contribution is PRODIGE: a method that learns a weighted graph representation of data end-to-end by gradient descent.
Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data
In this paper, we introduce Neural Oblivious Decision Ensembles (NODE), a new deep learning architecture, designed to work with any tabular data.
Relevance Proximity Graphs for Fast Relevance Retrieval
In plenty of machine learning applications, the most relevant items for a particular query should be efficiently extracted, while the relevance function is based on a highly-nonlinear model, e. g., DNNs or GBDTs.
Unsupervised Neural Quantization for Compressed-Domain Similarity Search
We tackle the problem of unsupervised visual descriptors compression, which is a key ingredient of large-scale image retrieval systems.
Non-metric Similarity Graphs for Maximum Inner Product Search
In this paper we address the problem of Maximum Inner Product Search (MIPS) that is currently the computational bottleneck in a large number of machine learning applications.
