353 citations · 1.8k across the 69 of their papers we have counts for
6 papers · 1 filter
Self-supervised learning with rotation-invariant kernels
Léon Zheng, Gilles Puy, Elisa Riccietti +2
We introduce a regularization loss based on kernel mean embeddings with rotation-invariant kernels on the hypersphere (also known as dot-product kernels) for self-supervised learni…
And the Bit Goes Down: Revisiting the Quantization of Neural Networks
Pierre Stock, Armand Joulin, Rémi Gribonval +2
In this paper, we address the problem of reducing the memory footprint of convolutional network architectures. We introduce a vector quantization method that aims at preserving the…
Equi-normalization of Neural Networks
Pierre Stock, Benjamin Graham, Rémi Gribonval +1
Modern neural networks are over-parametrized. In particular, each rectified linear hidden unit can be modified by a multiplicative factor by adjusting input and output weights, wit…
Learning a Complete Image Indexing Pipeline
Himalaya Jain, Joaquin Zepeda, Patrick Pérez +1
To work at scale, a complete image indexing system comprises two components: An inverted file index to restrict the actual search to only a subset that should contain most of the i…
SUBIC: A supervised, structured binary code for image search
Himalaya Jain, Joaquin Zepeda, Patrick Pérez +1
For large-scale visual search, highly compressed yet meaningful representations of images are essential. Structured vector quantizers based on product quantization and its variants…
Approximate search with quantized sparse representations
Himalaya Jain, Patrick Pérez, Rémi Gribonval +2
This paper tackles the task of storing a large collection of vectors, such as visual descriptors, and of searching in it. To this end, we propose to approximate database vectors by…