22k citations · 25.1k across the 40 of their papers we have counts for
44 papers
SI-Score: An image dataset for fine-grained analysis of robustness to object location, rotation and size
Jessica Yung, Rob Romijnders, Alexander Kolesnikov +6
Before deploying machine learning models it is critical to assess their robustness. In the context of deep neural networks for image understanding, changing the object location, ro…
Comparing Transfer and Meta Learning Approaches on a Unified Few-Shot Classification Benchmark
Vincent Dumoulin, Neil Houlsby, Utku Evci +4
Meta and transfer learning are two successful families of approaches to few-shot learning. Despite highly related goals, state-of-the-art advances in each family are measured large…
A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation
Francesco Locatello, Stefan Bauer, Mario Lucic +4
The idea behind the \emph{unsupervised} learning of \emph{disentangled} representations is that real-world data is generated by a few explanatory factors of variation which can be…
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov +9
While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited. In vision, attention…
Scalable Transfer Learning with Expert Models
Joan Puigcerver, Carlos Riquelme, Basil Mustafa +5
Transfer of pre-trained representations can improve sample efficiency and reduce computational requirements for new tasks. However, representations used for transfer are usually ge…
A Commentary on the Unsupervised Learning of Disentangled Representations
Francesco Locatello, Stefan Bauer, Mario Lucic +4
The goal of the unsupervised learning of disentangled representations is to separate the independent explanatory factors of variation in the data without access to supervision. In…
On Robustness and Transferability of Convolutional Neural Networks
Josip Djolonga, Jessica Yung, Michael Tschannen +11
Modern deep convolutional networks (CNNs) are often criticized for not generalizing under distributional shifts. However, several recent breakthroughs in transfer learning suggest…
What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study
Marcin Andrychowicz, Anton Raichuk, Piotr Stańczyk +9
In recent years, on-policy reinforcement learning (RL) has been successfully applied to many different continuous control tasks. While RL algorithms are often conceptually simple,…
What Do Neural Networks Learn When Trained With Random Labels?
Hartmut Maennel, Ibrahim Alabdulmohsin, Ilya Tolstikhin +4
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 memorizati…
Predicting Neural Network Accuracy from Weights
Thomas Unterthiner, Daniel Keysers, Sylvain Gelly +2
We show experimentally that the accuracy of a trained neural network can be predicted surprisingly well by looking only at its weights, without evaluating it on input data. We moti…
On Last-Layer Algorithms for Classification: Decoupling Representation from Uncertainty Estimation
Nicolas Brosse, Carlos Riquelme, Alice Martin +2
Uncertainty quantification for deep learning is a challenging open problem. Bayesian statistics offer a mathematically grounded framework to reason about uncertainties; however, ap…
Big Transfer (BiT): General Visual Representation Learning
Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai +4
Transfer of pre-trained representations improves sample efficiency and simplifies hyperparameter tuning when training deep neural networks for vision. We revisit the paradigm of pr…
Self-Supervised Learning of Video-Induced Visual Invariances
Michael Tschannen, Josip Djolonga, Marvin Ritter +5
We propose a general framework for self-supervised learning of transferable visual representations based on Video-Induced Visual Invariances (VIVI). We consider the implicit hierar…
Semantic Bottleneck Scene Generation
Samaneh Azadi, Michael Tschannen, Eric Tzeng +3
Coupling the high-fidelity generation capabilities of label-conditional image synthesis methods with the flexibility of unconditional generative models, we propose a semantic bottl…
A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark
Xiaohua Zhai, Joan Puigcerver, Alexander Kolesnikov +14
Representation learning promises to unlock deep learning for the long tail of vision tasks without expensive labelled datasets. Yet, the absence of a unified evaluation for general…
On Mutual Information Maximization for Representation Learning
Michael Tschannen, Josip Djolonga, Paul K. Rubenstein +2
Many recent methods for unsupervised or self-supervised representation learning train feature extractors by maximizing an estimate of the mutual information (MI) between different…
Google Research Football: A Novel Reinforcement Learning Environment
Karol Kurach, Anton Raichuk, Piotr Stańczyk +8
Recent progress in the field of reinforcement learning has been accelerated by virtual learning environments such as video games, where novel algorithms and ideas can be quickly te…
MULEX: Disentangling Exploitation from Exploration in Deep RL
Lucas Beyer, Damien Vincent, Olivier Teboul +3
An agent learning through interactions should balance its action selection process between probing the environment to discover new rewards and using the information acquired in the…
Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates
Hugo Penedones, Carlos Riquelme, Damien Vincent +5
We consider the core reinforcement-learning problem of on-policy value function approximation from a batch of trajectory data, and focus on various issues of Temporal Difference (T…
When can unlabeled data improve the learning rate?
Christina Göpfert, Shai Ben-David, Olivier Bousquet +3
In semi-supervised classification, one is given access both to labeled and unlabeled data. As unlabeled data is typically cheaper to acquire than labeled data, this setup becomes a…
Precision-Recall Curves Using Information Divergence Frontiers
Josip Djolonga, Mario Lucic, Marco Cuturi +3
Despite the tremendous progress in the estimation of generative models, the development of tools for diagnosing their failures and assessing their performance has advanced at a muc…
High-Fidelity Image Generation With Fewer Labels
Mario Lucic, Michael Tschannen, Marvin Ritter +3
Deep generative models are becoming a cornerstone of modern machine learning. Recent work on conditional generative adversarial networks has shown that learning complex, high-dimen…
Breaking the Softmax Bottleneck via Learnable Monotonic Pointwise Non-linearities
Octavian-Eugen Ganea, Sylvain Gelly, Gary Bécigneul +1
The Softmax function on top of a final linear layer is the de facto method to output probability distributions in neural networks. In many applications such as language models or t…
Parameter-Efficient Transfer Learning for NLP
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski +5
Fine-tuning large pre-trained models is an effective transfer mechanism in NLP. However, in the presence of many downstream tasks, fine-tuning is parameter inefficient: an entire n…