research
∙
06/01/2023
Birth of a Transformer: A Memory Viewpoint
Large language models based on transformers have achieved great empirica...
share
research
∙
04/14/2023
DINOv2: Learning Robust Visual Features without Supervision
The recent breakthroughs in natural language processing for model pretra...
research
∙
03/27/2023
The Stable Signature: Rooting Watermarks in Latent Diffusion Models
Generative image modeling enables a wide range of applications but raise...
research
∙
01/26/2023
Improving Statistical Fidelity for Neural Image Compression with Implicit Local Likelihood Models
Lossy image compression aims to represent images in as few bits as possi...
research
∙
12/14/2022
Image Compression with Product Quantized Masked Image Modeling
Recent neural compression methods have been based on the popular hyperpr...
research
∙
12/09/2022
Co-training 2^L Submodels for Visual Recognition
We introduce submodel co-training, a regularization method related to co...
research
∙
10/05/2022
Active Image Indexing
Image copy detection and retrieval from large databases leverage two com...
research
∙
04/14/2022
DeiT III: Revenge of the ViT
A Vision Transformer (ViT) is a simple neural architecture amenable to s...
research
∙
03/18/2022
Three things everyone should know about Vision Transformers
After their initial success in natural language processing, transformer ...
research
∙
12/27/2021
Augmenting Convolutional networks with attention-based aggregation
We show how to augment any convolutional network with an attention-based...
research
∙
12/20/2021
Are Large-scale Datasets Necessary for Self-Supervised Pre-training?
Pre-training models on large scale datasets, like ImageNet, is a standar...
research
∙
12/17/2021
Watermarking Images in Self-Supervised Latent Spaces
We revisit watermarking techniques based on pre-trained deep networks, i...
research
∙
12/17/2021
Nearest neighbor search with compact codes: A decoder perspective
Modern approaches for fast retrieval of similar vectors on billion-scale...
research
∙
10/01/2021
ResNet strikes back: An improved training procedure in timm
The influential Residual Networks designed by He et al. remain the gold-...
research
∙
06/17/2021
XCiT: Cross-Covariance Image Transformers
Following their success in natural language processing, transformers hav...
research
∙
05/07/2021
ResMLP: Feedforward networks for image classification with data-efficient training
We present ResMLP, an architecture built entirely upon multi-layer perce...
research
∙
04/29/2021
Emerging Properties in Self-Supervised Vision Transformers
In this paper, we question if self-supervised learning provides new prop...
research
∙
04/15/2021
Gradient-based Adversarial Attacks against Text Transformers
We propose the first general-purpose gradient-based attack against trans...
research
∙
04/02/2021
LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference
We design a family of image classification architectures that optimize t...
research
∙
03/31/2021
Going deeper with Image Transformers
Transformers have been recently adapted for large scale image classifica...
research
∙
02/10/2021
Training Vision Transformers for Image Retrieval
Transformers have shown outstanding results for natural language underst...
research
∙
12/23/2020
Training data-efficient image transformers distillation through attention
Recently, neural networks purely based on attention were shown to addres...
research
∙
11/25/2020
Grafit: Learning fine-grained image representations with coarse labels
This paper tackles the problem of learning a finer representation than t...
research
∙
08/13/2020
Powers of layers for image-to-image translation
We propose a simple architecture to address unpaired image-to-image tran...
research
∙
04/15/2020
Training with Quantization Noise for Extreme Model Compression
We tackle the problem of producing compact models, maximizing their accu...
research
∙
04/15/2020
Training with Quantization Noise for Extreme Fixed-Point Compression
We tackle the problem of producing compact models, maximizing their accu...
research
∙
03/18/2020
Fixing the train-test resolution discrepancy: FixEfficientNet
This note complements the paper "Fixing the train-test resolution discre...
research
∙
02/03/2020
Radioactive data: tracing through training
We want to detect whether a particular image dataset has been used to tr...
research
∙
08/29/2019
White-box vs Black-box: Bayes Optimal Strategies for Membership Inference
Membership inference determines, given a sample and trained parameters o...
research
∙
07/12/2019
And the Bit Goes Down: Revisiting the Quantization of Neural Networks
In this paper, we address the problem of reducing the memory footprint o...
research
∙
07/10/2019
Large Memory Layers with Product Keys
This paper introduces a structured memory which can be easily integrated...
research
∙
07/02/2019
Augmenting Self-attention with Persistent Memory
Transformer networks have lead to important progress in language modelin...
research
∙
06/14/2019
Fixing the train-test resolution discrepancy
Data-augmentation is key to the training of neural networks for image cl...
research
∙
05/02/2019
Billion-scale semi-supervised learning for image classification
This paper presents a study of semi-supervised learning with large convo...
research
∙
02/27/2019
Equi-normalization of Neural Networks
Modern neural networks are over-parametrized. In particular, each rectif...
research
∙
02/14/2019
MultiGrain: a unified image embedding for classes and instances
MultiGrain is a network architecture producing compact vector representa...
research
∙
11/27/2018
Understanding and Improving Kernel Local Descriptors
We propose a multiple-kernel local-patch descriptor based on efficient m...
research
∙
09/17/2018
Déjà Vu: an empirical evaluation of the memorization properties of ConvNets
Convolutional neural networks memorize part of their training data, whic...
research
∙
06/08/2018
A neural network catalyzer for multi-dimensional similarity search
This paper aims at learning a function mapping input vectors to an outpu...
research
∙
04/26/2018
Link and code: Fast indexing with graphs and compact regression codes
Similarity search approaches based on graph walks have recently attained...
research
∙
10/11/2017
Word Translation Without Parallel Data
State-of-the-art methods for learning cross-lingual word embeddings have...
research
∙
08/09/2017
An evaluation of large-scale methods for image instance and class discovery
This paper aims at discovering meaningful subsets of related images from...
research
∙
06/07/2017
Low-shot learning with large-scale diffusion
This paper considers the problem of inferring image labels for which onl...
research
∙
02/28/2017
Billion-scale similarity search with GPUs
Similarity search finds application in specialized database systems hand...
research
∙
12/12/2016
FastText.zip: Compressing text classification models
We consider the problem of producing compact architectures for text clas...
research
∙
11/24/2016
Interferences in match kernels
We consider the design of an image representation that embeds and aggreg...
research
∙
09/21/2016
How should we evaluate supervised hashing?
Hashing produces compact representations for documents, to perform tasks...
research
∙
09/14/2016
Efficient softmax approximation for GPUs
We propose an approximate strategy to efficiently train neural network b...
research
∙
09/07/2016
Polysemous codes
This paper considers the problem of approximate nearest neighbor search ...
research
∙
08/10/2016