Timezone: »
The idea behind object-centric representation learning is that natural scenes can better be modeled as compositions of objects and their relations as opposed to distributed representations. This inductive bias can be injected into neural networks to potentially improve systematic generalization and performance of downstream tasks in scenes with multiple objects. In this paper, we train state-of-the-art unsupervised models on five common multi-object datasets and evaluate segmentation metrics and downstream object property prediction. In addition, we study generalization and robustness by investigating the settings where either a single object is out of distribution -- e.g., having an unseen color, texture, or shape -- or global properties of the scene are altered -- e.g., by occlusions, cropping, or increasing the number of objects. From our experimental study, we find object-centric representations to be useful for downstream tasks and generally robust to most distribution shifts affecting objects. However, when the distribution shift affects the input in a less structured manner, robustness in terms of segmentation and downstream task performance may vary significantly across models and distribution shifts.
Author Information
Andrea Dittadi (Technical University of Denmark)
Samuele Papa (University of Amsterdam)
I have a background in information engineering, computer engineering, and artificial intelligence. In 2021 I completed both an MSc (cum laude) in Computer Engineering at the University of Padova and an MSc in Human-Centered Artificial Intelligence at the Technical University of Denmark. During my studies, I focused on fundamental research in the field of Deep Learning, specifically on how to obtain useful representations of images to enable the automation of higher-level cognitive tasks. I am now a PhD candidate under the POP-AART Lab (2021-2024), a collaboration between Elekta, the University of Amsterdam, and the Netherlands Cancer Institute. The aim of the collaboration is personalized online radiotherapy using artificial intelligence methods. The lab is supervised by Jan-Jakob Sonke and Efstratios Gavves. I will focus on using deep generative models to improve the quality of Cone Beam Computed Tomography (CBCT) while enforcing geometric and pathological integrity.
Michele De Vita (DTU)
Bernhard Schölkopf (MPI for Intelligent Systems Tübingen, Germany)
Bernhard Scholkopf received degrees in mathematics (London) and physics (Tubingen), and a doctorate in computer science from the Technical University Berlin. He has researched at AT&T Bell Labs, at GMD FIRST, Berlin, at the Australian National University, Canberra, and at Microsoft Research Cambridge (UK). In 2001, he was appointed scientific member of the Max Planck Society and director at the MPI for Biological Cybernetics; in 2010 he founded the Max Planck Institute for Intelligent Systems. For further information, see www.kyb.tuebingen.mpg.de/~bs.
Ole Winther (DTU and KU)
Francesco Locatello (Amazon Lablet)
Related Events (a corresponding poster, oral, or spotlight)
-
2022 Spotlight: Generalization and Robustness Implications in Object-Centric Learning »
Wed. Jul 20th 06:20 -- 06:25 PM Room Hall G
More from the Same Authors
-
2021 : On the Fairness of Causal Algorithmic Recourse »
Julius von Kügelgen · Amir Karimi · Umang Bhatt · Isabel Valera · Adrian Weller · Bernhard Schölkopf · Amir-Hossein Karimi -
2021 : Algorithmic Recourse in Partially and Fully Confounded Settings Through Bounding Counterfactual Effects »
Julius von Kügelgen · Nikita Agarwal · Jakob Zeitler · Afsaneh Mastouri · Bernhard Schölkopf -
2021 : Representation Learning for Out-of-distribution Generalization in Downstream Tasks »
Frederik Träuble · Andrea Dittadi · Manuel Wuthrich · Felix Widmaier · Peter V Gehler · Ole Winther · Francesco Locatello · Olivier Bachem · Bernhard Schölkopf · Stefan Bauer -
2021 : Representation Learning for Out-of-distribution Generalization in Downstream Tasks »
Frederik Träuble · Andrea Dittadi · Manuel Wüthrich · Felix Widmaier · Peter Gehler · Ole Winther · Francesco Locatello · Olivier Bachem · Bernhard Schölkopf · Stefan Bauer -
2021 : Lie interventions in complex systems with cycles »
Michel Besserve · Bernhard Schölkopf -
2022 : Maximum Mean Discrepancy Distributionally Robust Nonlinear Chance-Constrained Optimization with Finite-Sample Guarantee »
Yassine Nemmour · Heiner Kremer · Bernhard Schölkopf · Jia-Jie Zhu -
2022 : Inductive Biases for Object-Centric Representations in the Presence of Complex Textures »
Samuele Papa · Ole Winther · Andrea Dittadi -
2022 : Invited talks I, Q/A »
Bernhard Schölkopf · David Lopez-Paz -
2022 : Invited Talks 1, Bernhard Schölkopf and David Lopez-Paz »
Bernhard Schölkopf · David Lopez-Paz -
2022 Poster: Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models »
Paul Rolland · Volkan Cevher · Matthäus Kleindessner · Chris Russell · Dominik Janzing · Bernhard Schölkopf · Francesco Locatello -
2022 Oral: Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models »
Paul Rolland · Volkan Cevher · Matthäus Kleindessner · Chris Russell · Dominik Janzing · Bernhard Schölkopf · Francesco Locatello -
2022 Poster: Action-Sufficient State Representation Learning for Control with Structural Constraints »
Biwei Huang · Chaochao Lu · Liu Leqi · Jose Miguel Hernandez-Lobato · Clark Glymour · Bernhard Schölkopf · Kun Zhang -
2022 Poster: SCHA-VAE: Hierarchical Context Aggregation for Few-Shot Generation »
Giorgio Giannone · Ole Winther -
2022 Spotlight: SCHA-VAE: Hierarchical Context Aggregation for Few-Shot Generation »
Giorgio Giannone · Ole Winther -
2022 Spotlight: Action-Sufficient State Representation Learning for Control with Structural Constraints »
Biwei Huang · Chaochao Lu · Liu Leqi · Jose Miguel Hernandez-Lobato · Clark Glymour · Bernhard Schölkopf · Kun Zhang -
2022 Poster: Causal Inference Through the Structural Causal Marginal Problem »
Luigi Gresele · Julius von Kügelgen · Jonas Kübler · Elke Kirschbaum · Bernhard Schölkopf · Dominik Janzing -
2022 Poster: Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions »
Heiner Kremer · Jia-Jie Zhu · Krikamol Muandet · Bernhard Schölkopf -
2022 Poster: On the Adversarial Robustness of Causal Algorithmic Recourse »
Ricardo Dominguez-Olmedo · Amir Karimi · Bernhard Schölkopf -
2022 Spotlight: Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions »
Heiner Kremer · Jia-Jie Zhu · Krikamol Muandet · Bernhard Schölkopf -
2022 Spotlight: Causal Inference Through the Structural Causal Marginal Problem »
Luigi Gresele · Julius von Kügelgen · Jonas Kübler · Elke Kirschbaum · Bernhard Schölkopf · Dominik Janzing -
2022 Spotlight: On the Adversarial Robustness of Causal Algorithmic Recourse »
Ricardo Dominguez-Olmedo · Amir Karimi · Bernhard Schölkopf -
2021 Poster: Function Contrastive Learning of Transferable Meta-Representations »
Muhammad Waleed Gondal · Shruti Joshi · Nasim Rahaman · Stefan Bauer · Manuel Wuthrich · Bernhard Schölkopf -
2021 Spotlight: Function Contrastive Learning of Transferable Meta-Representations »
Muhammad Waleed Gondal · Shruti Joshi · Nasim Rahaman · Stefan Bauer · Manuel Wuthrich · Bernhard Schölkopf -
2021 Poster: On Disentangled Representations Learned from Correlated Data »
Frederik Träuble · Elliot Creager · Niki Kilbertus · Francesco Locatello · Andrea Dittadi · Anirudh Goyal · Bernhard Schölkopf · Stefan Bauer -
2021 Poster: Bayesian Quadrature on Riemannian Data Manifolds »
Christian Fröhlich · Alexandra Gessner · Philipp Hennig · Bernhard Schölkopf · Georgios Arvanitidis -
2021 Spotlight: Bayesian Quadrature on Riemannian Data Manifolds »
Christian Fröhlich · Alexandra Gessner · Philipp Hennig · Bernhard Schölkopf · Georgios Arvanitidis -
2021 Oral: On Disentangled Representations Learned from Correlated Data »
Frederik Träuble · Elliot Creager · Niki Kilbertus · Francesco Locatello · Andrea Dittadi · Anirudh Goyal · Bernhard Schölkopf · Stefan Bauer -
2021 Poster: Necessary and sufficient conditions for causal feature selection in time series with latent common causes »
Atalanti Mastakouri · Bernhard Schölkopf · Dominik Janzing -
2021 Poster: Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression »
Junhyung Park · Uri Shalit · Bernhard Schölkopf · Krikamol Muandet -
2021 Spotlight: Necessary and sufficient conditions for causal feature selection in time series with latent common causes »
Atalanti Mastakouri · Bernhard Schölkopf · Dominik Janzing -
2021 Spotlight: Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression »
Junhyung Park · Uri Shalit · Bernhard Schölkopf · Krikamol Muandet -
2021 Poster: Causal Curiosity: RL Agents Discovering Self-supervised Experiments for Causal Representation Learning »
Sumedh Sontakke · Arash Mehrjou · Laurent Itti · Bernhard Schölkopf -
2021 Spotlight: Causal Curiosity: RL Agents Discovering Self-supervised Experiments for Causal Representation Learning »
Sumedh Sontakke · Arash Mehrjou · Laurent Itti · Bernhard Schölkopf -
2020 Workshop: Inductive Biases, Invariances and Generalization in Reinforcement Learning »
Anirudh Goyal · Rosemary Nan Ke · Jane Wang · Stefan Bauer · Theophane Weber · Fabio Viola · Bernhard Schölkopf · Stefan Bauer -
2020 Poster: Weakly-Supervised Disentanglement Without Compromises »
Francesco Locatello · Ben Poole · Gunnar Ratsch · Bernhard Schölkopf · Olivier Bachem · Michael Tschannen -
2019 Poster: Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness »
Raphael Suter · Djordje Miladinovic · Bernhard Schölkopf · Stefan Bauer -
2019 Oral: Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness »
Raphael Suter · Djordje Miladinovic · Bernhard Schölkopf · Stefan Bauer -
2019 Poster: Kernel Mean Matching for Content Addressability of GANs »
Wittawat Jitkrittum · Wittawat Jitkrittum · Patsorn Sangkloy · Muhammad Waleed Gondal · Amit Raj · James Hays · Bernhard Schölkopf -
2019 Oral: Kernel Mean Matching for Content Addressability of GANs »
Wittawat Jitkrittum · Wittawat Jitkrittum · Patsorn Sangkloy · Patsorn Sangkloy · Muhammad Waleed Gondal · Muhammad Waleed Gondal · Amit Raj · Amit Raj · James Hays · James Hays · Bernhard Schölkopf · Bernhard Schölkopf -
2019 Poster: First-Order Adversarial Vulnerability of Neural Networks and Input Dimension »
Carl-Johann Simon-Gabriel · Yann Ollivier · Leon Bottou · Bernhard Schölkopf · David Lopez-Paz -
2019 Poster: Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations »
Francesco Locatello · Stefan Bauer · Mario Lucic · Gunnar Ratsch · Sylvain Gelly · Bernhard Schölkopf · Olivier Bachem -
2019 Oral: First-Order Adversarial Vulnerability of Neural Networks and Input Dimension »
Carl-Johann Simon-Gabriel · Yann Ollivier · Leon Bottou · Bernhard Schölkopf · David Lopez-Paz -
2019 Oral: Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations »
Francesco Locatello · Stefan Bauer · Mario Lucic · Gunnar Ratsch · Sylvain Gelly · Bernhard Schölkopf · Olivier Bachem -
2018 Poster: Detecting non-causal artifacts in multivariate linear regression models »
Dominik Janzing · Bernhard Schölkopf -
2018 Poster: On Matching Pursuit and Coordinate Descent »
Francesco Locatello · Anant Raj · Sai Praneeth Reddy Karimireddy · Gunnar Ratsch · Bernhard Schölkopf · Sebastian Stich · Martin Jaggi -
2018 Oral: Detecting non-causal artifacts in multivariate linear regression models »
Dominik Janzing · Bernhard Schölkopf -
2018 Oral: On Matching Pursuit and Coordinate Descent »
Francesco Locatello · Anant Raj · Sai Praneeth Reddy Karimireddy · Gunnar Ratsch · Bernhard Schölkopf · Sebastian Stich · Martin Jaggi -
2018 Poster: Tempered Adversarial Networks »
Mehdi S. M. Sajjadi · Giambattista Parascandolo · Arash Mehrjou · Bernhard Schölkopf -
2018 Poster: Differentially Private Database Release via Kernel Mean Embeddings »
Matej Balog · Ilya Tolstikhin · Bernhard Schölkopf -
2018 Oral: Differentially Private Database Release via Kernel Mean Embeddings »
Matej Balog · Ilya Tolstikhin · Bernhard Schölkopf -
2018 Oral: Tempered Adversarial Networks »
Mehdi S. M. Sajjadi · Giambattista Parascandolo · Arash Mehrjou · Bernhard Schölkopf -
2018 Poster: Learning Independent Causal Mechanisms »
Giambattista Parascandolo · Niki Kilbertus · Mateo Rojas-Carulla · Bernhard Schölkopf -
2018 Oral: Learning Independent Causal Mechanisms »
Giambattista Parascandolo · Niki Kilbertus · Mateo Rojas-Carulla · Bernhard Schölkopf -
2017 Invited Talk: Causal Learning »
Bernhard Schölkopf