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Daniel Keysers

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6 papers

2 author rows

ICML Conference 2023 Conference Paper

Scaling Vision Transformers to 22 Billion Parameters

Mostafa Dehghani 0001
Josip Djolonga
Basil Mustafa
Piotr Padlewski
Jonathan Heek
Justin Gilmer
Andreas Peter Steiner
Mathilde Caron

The scaling of Transformers has driven breakthrough capabilities for language models. At present, the largest large language models (LLMs) contain upwards of 100B parameters. Vision Transformers (ViT) have introduced the same architecture to image and video modelling, but these have not yet been successfully scaled to nearly the same degree; the largest dense ViT contains 4B parameters (Chen et al. , 2022). We present a recipe for highly efficient and stable training of a 22B-parameter ViT (ViT-22B) and perform a wide variety of experiments on the resulting model. When evaluated on downstream tasks (often with a lightweight linear model on frozen features), ViT-22B demonstrates increasing performance with scale. We further observe other interesting benefits of scale, including an improved tradeoff between fairness and performance, state-of-the-art alignment to human visual perception in terms of shape/texture bias, and improved robustness. ViT-22B demonstrates the potential for "LLM-like" scaling in vision, and provides key steps towards getting there.

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NeurIPS Conference 2021 Conference Paper

MLP-Mixer: An all-MLP Architecture for Vision

Ilya O. Tolstikhin
Neil Houlsby
Alexander Kolesnikov
Lucas Beyer
Xiaohua Zhai
Thomas Unterthiner
Jessica Yung
Andreas Steiner

Convolutional Neural Networks (CNNs) are the go-to model for computer vision. Recently, attention-based networks, such as the Vision Transformer, have also become popular. In this paper we show that while convolutions and attention are both sufficient for good performance, neither of them are necessary. We present MLP-Mixer, an architecture based exclusively on multi-layer perceptrons (MLPs). MLP-Mixer contains two types of layers: one with MLPs applied independently to image patches (i. e. "mixing" the per-location features), and one with MLPs applied across patches (i. e. "mixing" spatial information). When trained on large datasets, or with modern regularization schemes, MLP-Mixer attains competitive scores on image classification benchmarks, with pre-training and inference cost comparable to state-of-the-art models. We hope that these results spark further research beyond the realms of well established CNNs and Transformers.

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ICLR Conference 2021 Conference Paper

Scalable Transfer Learning with Expert Models

Joan Puigcerver
Carlos Riquelme
Basil Mustafa
Cédric Renggli
André Susano Pinto
Sylvain Gelly
Daniel Keysers
Neil Houlsby

Transfer of pre-trained representations can improve sample efficiency and reduce computational requirements for new tasks. However, representations used for transfer are usually generic, and are not tailored to a particular distribution of downstream tasks. We explore the use of expert representations for transfer with a simple, yet effective, strategy. We train a diverse set of experts by exploiting existing label structures, and use cheap-to-compute performance proxies to select the relevant expert for each target task. This strategy scales the process of transferring to new tasks, since it does not revisit the pre-training data during transfer. Accordingly, it requires little extra compute per target task, and results in a speed-up of 2-3 orders of magnitude compared to competing approaches. Further, we provide an adapter-based architecture able to compress many experts into a single model. We evaluate our approach on two different data sources and demonstrate that it outperforms baselines on over 20 diverse vision tasks in both cases.

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NeurIPS Conference 2021 Conference Paper

Scaling Vision with Sparse Mixture of Experts

Carlos Riquelme
Joan Puigcerver
Basil Mustafa
Maxim Neumann
Rodolphe Jenatton
André Susano Pinto
Daniel Keysers
Neil Houlsby

Sparsely-gated Mixture of Experts networks (MoEs) have demonstrated excellent scalability in Natural Language Processing. In Computer Vision, however, almost all performant networks are "dense", that is, every input is processed by every parameter. We present a Vision MoE (V-MoE), a sparse version of the Vision Transformer, that is scalable and competitive with the largest dense networks. When applied to image recognition, V-MoE matches the performance of state-of-the-art networks, while requiring as little as half of the compute at inference time. Further, we propose an extension to the routing algorithm that can prioritize subsets of each input across the entire batch, leading to adaptive per-image compute. This allows V-MoE to trade-off performance and compute smoothly at test-time. Finally, we demonstrate the potential of V-MoE to scale vision models, and train a 15B parameter model that attains 90. 35% on ImageNet.

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ICLR Conference 2020 Conference Paper

Measuring Compositional Generalization: A Comprehensive Method on Realistic Data

Daniel Keysers
Nathanael Schärli
Nathan Scales
Hylke Buisman
Daniel Furrer
Sergii Kashubin
Nikola Momchev
Danila Sinopalnikov

State-of-the-art machine learning methods exhibit limited compositional generalization. At the same time, there is a lack of realistic benchmarks that comprehensively measure this ability, which makes it challenging to find and evaluate improvements. We introduce a novel method to systematically construct such benchmarks by maximizing compound divergence while guaranteeing a small atom divergence between train and test sets, and we quantitatively compare this method to other approaches for creating compositional generalization benchmarks. We present a large and realistic natural language question answering dataset that is constructed according to this method, and we use it to analyze the compositional generalization ability of three machine learning architectures. We find that they fail to generalize compositionally and that there is a surprisingly strong negative correlation between compound divergence and accuracy. We also demonstrate how our method can be used to create new compositionality benchmarks on top of the existing SCAN dataset, which confirms these findings.

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NeurIPS Conference 2020 Conference Paper

What Do Neural Networks Learn When Trained With Random Labels?

Hartmut Maennel
Ibrahim M. Alabdulmohsin
Ilya O. Tolstikhin
Robert Baldock
Olivier Bousquet
Sylvain Gelly
Daniel Keysers

We study deep neural networks (DNNs) trained on natural image data with entirely random labels. Despite its popularity in the literature, where it is often used to study memorization, generalization, and other phenomena, little is known about what DNNs learn in this setting. In this paper, we show analytically for convolutional and fully connected networks that an alignment between the principal components of network parameters and data takes place when training with random labels. We study this alignment effect by investigating neural networks pre-trained on randomly labelled image data and subsequently fine-tuned on disjoint datasets with random or real labels. We show how this alignment produces a positive transfer: networks pre-trained with random labels train faster downstream compared to training from scratch even after accounting for simple effects, such as weight scaling. We analyze how competing effects, such as specialization at later layers, may hide the positive transfer. These effects are studied in several network architectures, including VGG16 and ResNet18, on CIFAR10 and ImageNet.

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