Search Results for author: Cian Eastwood

Found 10 papers, 5 papers with code

GIVT: Generative Infinite-Vocabulary Transformers

no code implementations4 Dec 2023 Michael Tschannen, Cian Eastwood, Fabian Mentzer

We introduce generative infinite-vocabulary transformers (GIVT) which generate vector sequences with real-valued entries, instead of discrete tokens from a finite vocabulary.

Conditional Image Generation Depth Estimation +1

Spuriosity Didn't Kill the Classifier: Using Invariant Predictions to Harness Spurious Features

no code implementations19 Jul 2023 Cian Eastwood, Shashank Singh, Andrei Liviu Nicolicioiu, Marin Vlastelica, Julius von Kügelgen, Bernhard Schölkopf

To avoid failures on out-of-distribution data, recent works have sought to extract features that have an invariant or stable relationship with the label across domains, discarding "spurious" or unstable features whose relationship with the label changes across domains.


Probable Domain Generalization via Quantile Risk Minimization

2 code implementations20 Jul 2022 Cian Eastwood, Alexander Robey, Shashank Singh, Julius von Kügelgen, Hamed Hassani, George J. Pappas, Bernhard Schölkopf

By minimizing the $\alpha$-quantile of predictor's risk distribution over domains, QRM seeks predictors that perform well with probability $\alpha$.

Domain Generalization Test

Align-Deform-Subtract: An Interventional Framework for Explaining Object Differences

no code implementations9 Mar 2022 Cian Eastwood, Li Nanbo, Christopher K. I. Williams

Given two object images, how can we explain their differences in terms of the underlying object properties?

counterfactual Object

Learning Object-Centric Representations of Multi-Object Scenes from Multiple Views

1 code implementation NeurIPS 2020 Li Nanbo, Cian Eastwood, Robert B. Fisher

In order to sidestep the main technical difficulty of the multi-object-multi-view scenario -- maintaining object correspondences across views -- MulMON iteratively updates the latent object representations for a scene over multiple views.

Object Scene Understanding

Unit-level surprise in neural networks

no code implementations NeurIPS Workshop ICBINB 2021 Cian Eastwood, Ian Mason, Chris Williams

To adapt to changes in real-world data distributions, neural networks must update their parameters.

Source-Free Adaptation to Measurement Shift via Bottom-Up Feature Restoration

1 code implementation ICLR 2022 Cian Eastwood, Ian Mason, Christopher K. I. Williams, Bernhard Schölkopf

Existing methods for SFDA leverage entropy-minimization techniques which: (i) apply only to classification; (ii) destroy model calibration; and (iii) rely on the source model achieving a good level of feature-space class-separation in the target domain.

Source-Free Domain Adaptation

A Framework for the Quantitative Evaluation of Disentangled Representations

2 code implementations ICLR 2018 Cian Eastwood, Christopher K. I. Williams

Recent AI research has emphasised the importance of learning disentangled representations of the explanatory factors behind data.

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