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08/24/2023
Code Llama: Open Foundation Models for Code
We release Code Llama, a family of large language models for code based ...
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07/18/2023
Llama 2: Open Foundation and Fine-Tuned Chat Models
In this work, we develop and release Llama 2, a collection of pretrained...
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02/27/2023
LLaMA: Open and Efficient Foundation Language Models
We introduce LLaMA, a collection of foundation language models ranging f...
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12/09/2022
Co-training 2^L Submodels for Visual Recognition
We introduce submodel co-training, a regularization method related to co...
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04/14/2022
DeiT III: Revenge of the ViT
A Vision Transformer (ViT) is a simple neural architecture amenable to s...
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03/18/2022
Three things everyone should know about Vision Transformers
After their initial success in natural language processing, transformer ...
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12/27/2021
Augmenting Convolutional networks with attention-based aggregation
We show how to augment any convolutional network with an attention-based...
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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...
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10/01/2021
ResNet strikes back: An improved training procedure in timm
The influential Residual Networks designed by He et al. remain the gold-...
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06/17/2021
XCiT: Cross-Covariance Image Transformers
Following their success in natural language processing, transformers hav...
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05/07/2021
ResMLP: Feedforward networks for image classification with data-efficient training
We present ResMLP, an architecture built entirely upon multi-layer perce...
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04/29/2021
Emerging Properties in Self-Supervised Vision Transformers
In this paper, we question if self-supervised learning provides new prop...
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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...
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03/31/2021
Going deeper with Image Transformers
Transformers have been recently adapted for large scale image classifica...
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03/19/2021
ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases
Convolutional architectures have proven extremely successful for vision ...
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12/23/2020
Training data-efficient image transformers distillation through attention
Recently, neural networks purely based on attention were shown to addres...
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11/25/2020
Grafit: Learning fine-grained image representations with coarse labels
This paper tackles the problem of learning a finer representation than t...
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08/13/2020
Powers of layers for image-to-image translation
We propose a simple architecture to address unpaired image-to-image tran...
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03/18/2020
Fixing the train-test resolution discrepancy: FixEfficientNet
This note complements the paper "Fixing the train-test resolution discre...
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06/14/2019
