We also show that, due to the use of iterative variational inference, our system is able to learn multi-modal posteriors for ambiguous inputs and extends naturally to sequences. R /PageLabels Work fast with our official CLI. Provide values for the following variables: Monitor loss curves and visualize RGB components/masks: If you would like to skip training and just play around with a pre-trained model, we provide the following pre-trained weights in ./examples: We found that on Tetrominoes and CLEVR in the Multi-Object Datasets benchmark, using GECO was necessary to stabilize training across random seeds and improve sample efficiency (in addition to using a few steps of lightweight iterative amortized inference). Klaus Greff,Raphal Lopez Kaufman,Rishabh Kabra,Nick Watters,Christopher Burgess,Daniel Zoran,Loic Matthey,Matthew Botvinick,Alexander Lerchner. We show that optimization challenges caused by requiring both symmetry and disentanglement can in fact be addressed by high-cost iterative amortized inference by designing the framework to minimize its dependence on it. 212-222. 33, On the Possibilities of AI-Generated Text Detection, 04/10/2023 by Souradip Chakraborty Multi-Object Representation Learning with Iterative Variational Inference Human perception is structured around objects which form the basis for o. Physical reasoning in infancy, Goel, Vikash, et al. << >> >> IEEE Transactions on Pattern Analysis and Machine Intelligence. We present Cascaded Variational Inference (CAVIN) Planner, a model-based method that hierarchically generates plans by sampling from latent spaces. You will need to make sure these env vars are properly set for your system first. Store the .h5 files in your desired location. representations. methods. See lib/datasets.py for how they are used. 03/01/19 - Human perception is structured around objects which form the basis for our higher-level cognition and impressive systematic genera. 2022 Poster: General-purpose, long-context autoregressive modeling with Perceiver AR While these results are very promising, several << Learn more about the CLI. In this work, we introduce EfficientMORL, an efficient framework for the unsupervised learning of object-centric representations. Multi-object representation learning has recently been tackled using unsupervised, VAE-based models. Human perception is structured around objects which form the basis for our higher-level cognition and impressive systematic generalization abilities. There is much evidence to suggest that objects are a core level of abstraction at which humans perceive and The dynamics and generative model are learned from experience with a simple environment (active multi-dSprites). Choosing the reconstruction target: I have come up with the following heuristic to quickly set the reconstruction target for a new dataset without investing much effort: Some other config parameters are omitted which are self-explanatory. Klaus Greff, Raphael Lopez Kaufman, Rishabh Kabra, Nick Watters, Chris Burgess, Daniel Zoran, Loic Matthey, Matthew Botvinick, Alexander Lerchner. ", Berner, Christopher, et al. Object representations are endowed with independent action-based dynamics. /Names endobj << R Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. However, we observe that methods for learning these representations are either impractical due to long training times and large memory consumption or forego key inductive biases. In this workshop we seek to build a consensus on what object representations should be by engaging with researchers understand the world [8,9]. We achieve this by performing probabilistic inference using a recurrent neural network. Furthermore, we aim to define concrete tasks and capabilities that agents building on Like with the training bash script, you need to set/check the following bash variables ./scripts/eval.sh: Results will be stored in files ARI.txt, MSE.txt and KL.txt in folder $OUT_DIR/results/{test.experiment_name}/$CHECKPOINT-seed=$SEED. The experiment_name is specified in the sacred JSON file. Unsupervised Learning of Object Keypoints for Perception and Control., Lin, Zhixuan, et al. Abstract. /MediaBox Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning, Mitigating Embedding and Class Assignment Mismatch in Unsupervised Image Classification, Improving Unsupervised Image Clustering With Robust Learning, InfoBot: Transfer and Exploration via the Information Bottleneck, Reinforcement Learning with Unsupervised Auxiliary Tasks, Learning Latent Dynamics for Planning from Pixels, Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images, DARLA: Improving Zero-Shot Transfer in Reinforcement Learning, Count-Based Exploration with Neural Density Models, Learning Actionable Representations with Goal-Conditioned Policies, Automatic Goal Generation for Reinforcement Learning Agents, VIME: Variational Information Maximizing Exploration, Unsupervised State Representation Learning in Atari, Learning Invariant Representations for Reinforcement Learning without Reconstruction, CURL: Contrastive Unsupervised Representations for Reinforcement Learning, DeepMDP: Learning Continuous Latent Space Models for Representation Learning, beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework, Isolating Sources of Disentanglement in Variational Autoencoders, InfoGAN: Interpretable Representation Learning byInformation Maximizing Generative Adversarial Nets, Spatial Broadcast Decoder: A Simple Architecture forLearning Disentangled Representations in VAEs, Challenging Common Assumptions in the Unsupervised Learning ofDisentangled Representations, Contrastive Learning of Structured World Models, Entity Abstraction in Visual Model-Based Reinforcement Learning, Reasoning About Physical Interactions with Object-Oriented Prediction and Planning, MONet: Unsupervised Scene Decomposition and Representation, Multi-Object Representation Learning with Iterative Variational Inference, GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations, Generative Modeling of Infinite Occluded Objects for Compositional Scene Representation, SPACE: Unsupervised Object-Oriented Scene Representation via Spatial Attention and Decomposition, COBRA: Data-Efficient Model-Based RL through Unsupervised Object Discovery and Curiosity-Driven Exploration, Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions, Unsupervised Video Object Segmentation for Deep Reinforcement Learning, Object-Oriented Dynamics Learning through Multi-Level Abstraction, Language as an Abstraction for Hierarchical Deep Reinforcement Learning, Interaction Networks for Learning about Objects, Relations and Physics, Learning Compositional Koopman Operators for Model-Based Control, Unmasking the Inductive Biases of Unsupervised Object Representations for Video Sequences, Workshop on Representation Learning for NLP. Yet most work on representation learning focuses on feature learning without even considering multiple objects, or treats segmentation as an (often supervised) preprocessing step. objects with novel feature combinations. ", Mnih, Volodymyr, et al. and represent objects jointly. Experiments show that InfoGAN learns interpretable representations that are competitive with representations learned by existing fully supervised methods. stream We found GECO wasn't needed for Multi-dSprites to achieve stable convergence across many random seeds and a good trade-off of reconstruction and KL. Gre, Klaus, et al. A tag already exists with the provided branch name. The motivation of this work is to design a deep generative model for learning high-quality representations of multi-object scenes. plan to build agents that are equally successful. 0 Multi-Object Representation Learning with Iterative Variational Inference 03/01/2019 by Klaus Greff, et al. ( G o o g l e) 1 "Playing atari with deep reinforcement learning. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. This work presents EGO, a conceptually simple and general approach to learning object-centric representations through an energy-based model and demonstrates the effectiveness of EGO in systematic compositional generalization, by re-composing learned energy functions for novel scene generation and manipulation. If nothing happens, download Xcode and try again. most work on representation learning focuses on feature learning without even Space: Unsupervised Object-Oriented Scene Representation via Spatial Attention and Decomposition., Bisk, Yonatan, et al. /Contents Corpus ID: 67855876; Multi-Object Representation Learning with Iterative Variational Inference @inproceedings{Greff2019MultiObjectRL, title={Multi-Object Representation Learning with Iterative Variational Inference}, author={Klaus Greff and Raphael Lopez Kaufman and Rishabh Kabra and Nicholas Watters and Christopher P. Burgess and Daniel Zoran and Lo{\"i}c Matthey and Matthew M. Botvinick and . We also show that, due to the use of iterative variational inference, our system is able to learn multi-modal posteriors for ambiguous inputs and extends naturally to sequences. In addition, object perception itself could benefit from being placed in an active loop, as . R The resulting framework thus uses two-stage inference. Multi-objective training of Generative Adversarial Networks with multiple discriminators ( IA, JM, TD, BC, THF, IM ), pp. Once foreground objects are discovered, the EMA of the reconstruction error should be lower than the target (in Tensorboard. We provide a bash script ./scripts/make_gifs.sh for creating disentanglement GIFs for individual slots. pr PaLM-E: An Embodied Multimodal Language Model, NeSF: Neural Semantic Fields for Generalizable Semantic Segmentation of representations, and how best to leverage them in agent training. 0 You signed in with another tab or window. occluded parts, and extrapolates to scenes with more objects and to unseen from developmental psychology. By clicking accept or continuing to use the site, you agree to the terms outlined in our. ] This model is able to segment visual scenes from complex 3D environments into distinct objects, learn disentangled representations of individual objects, and form consistent and coherent predictions of future frames, in a fully unsupervised manner and argues that when inferring scene structure from image sequences it is better to use a fixed prior. A zip file containing the datasets used in this paper can be downloaded from here. Our method learns -- without supervision -- to inpaint Recently developed deep learning models are able to learn to segment sce LAVAE: Disentangling Location and Appearance, Compositional Scene Modeling with Global Object-Centric Representations, On the Generalization of Learned Structured Representations, Fusing RGBD Tracking and Segmentation Tree Sampling for Multi-Hypothesis There is plenty of theoretical and empirical evidence that depth of neur Several variants of the Long Short-Term Memory (LSTM) architecture for Our method learns without supervision to inpaint occluded parts, and extrapolates to scenes with more objects and to unseen objects with novel feature combinations. 0 24, From Words to Music: A Study of Subword Tokenization Techniques in You can select one of the papers that has a tag similar to the tag in the schedule, e.g., any of the "bias & fairness" paper on a "bias & fairness" week. The following steps to start training a model can similarly be followed for CLEVR6 and Multi-dSprites. The fundamental challenge of planning for multi-step manipulation is to find effective and plausible action sequences that lead to the task goal. It has also been shown that objects are useful abstractions in designing machine learning algorithms for embodied agents. Volumetric Segmentation. In: 36th International Conference on Machine Learning, ICML 2019 2019-June . "Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm. Multi-Object Representation Learning with Iterative Variational Inference 2019-03-01 Klaus Greff, Raphal Lopez Kaufmann, Rishab Kabra, Nick Watters, Chris Burgess, Daniel Zoran, Loic Matthey, Matthew Botvinick, Alexander Lerchner arXiv_CV arXiv_CV Segmentation Represenation_Learning Inference Abstract << << They may be used effectively in a variety of important learning and control tasks, Note that we optimize unnormalized image likelihoods, which is why the values are negative. "Learning dexterous in-hand manipulation. perturbations and be able to rapidly generalize or adapt to novel situations. open problems remain. This accounts for a large amount of the reconstruction error. This is a recurring payment that will happen monthly, If you exceed more than 500 images, they will be charged at a rate of $5 per 500 images. /S 0 We present an approach for learning probabilistic, object-based representations from data, called the "multi-entity variational autoencoder" (MVAE). Generally speaking, we want a model that. higher-level cognition and impressive systematic generalization abilities. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. endobj >> sign in 1 GECO is an excellent optimization tool for "taming" VAEs that helps with two key aspects: The caveat is we have to specify the desired reconstruction target for each dataset, which depends on the image resolution and image likelihood. assumption that a scene is composed of multiple entities, it is possible to obj Are you sure you want to create this branch? Unzipped, the total size is about 56 GB. R Start training and monitor the reconstruction error (e.g., in Tensorboard) for the first 10-20% of training steps. {3Jo"K,`C%]5A?z?Ae!iZ{I6g9k?rW~gb*x"uOr ;x)Ny+sRVOaY)L fsz3O S'_O9L/s.5S_m -sl# 06vTCK@Q@5 m#DGtFQG u 9$-yAt6l2B.-|x"WlurQc;VkZ2*d1D spn.8+-pw 9>Q2yJe9SE3y}2!=R =?ApQ{,XAA_d0F. 0 Multi-object representation learning with iterative variational inference . "Learning synergies between pushing and grasping with self-supervised deep reinforcement learning. obj 0 and represent objects jointly. learn to segment images into interpretable objects with disentangled Multi-Object Representation Learning slots IODINE VAE (ours) Iterative Object Decomposition Inference NEtwork Built on the VAE framework Incorporates multi-object structure Iterative variational inference Decoder Structure Iterative Inference Iterative Object Decomposition Inference NEtwork Decoder Structure Unsupervised Video Object Segmentation for Deep Reinforcement Learning., Greff, Klaus, et al. ", Zeng, Andy, et al. ", Vinyals, Oriol, et al. For example, add this line to the end of the environment file: prefix: /home/{YOUR_USERNAME}/.conda/envs. 0 We recommend starting out getting familiar with this repo by training EfficientMORL on the Tetrominoes dataset. Recent advances in deep reinforcement learning and robotics have enabled agents to achieve superhuman performance on This is used to develop a new model, GENESIS-v2, which can infer a variable number of object representations without using RNNs or iterative refinement. This path will be printed to the command line as well. - Motion Segmentation & Multiple Object Tracking by Correlation Co-Clustering. A stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case is introduced. Human perception is structured around objects which form the basis for our higher-level cognition and impressive systematic generalization abilities. However, we observe that methods for learning these representations are either impractical due to long training times and large memory consumption or forego key inductive biases. A Behavioral Approach to Visual Navigation with Graph Localization Networks, Learning from Multiview Correlations in Open-Domain Videos. Check and update the same bash variables DATA_PATH, OUT_DIR, CHECKPOINT, ENV, and JSON_FILE as you did for computing the ARI+MSE+KL. representation of the world. The number of refinement steps taken during training is reduced following a curriculum, so that at test time with zero steps the model achieves 99.1% of the refined decomposition performance. Yet to use Codespaces. /Catalog 9 Trends in Integration of Vision and Language Research: A Survey of Tasks, Datasets, and Methods, arXiv 2019, Representation Learning: A Review and New Perspectives, TPAMI 2013, Self-supervised Learning: Generative or Contrastive, arxiv, Made: Masked autoencoder for distribution estimation, ICML 2015, Wavenet: A generative model for raw audio, arxiv, Pixel Recurrent Neural Networks, ICML 2016, Conditional Image Generation withPixelCNN Decoders, NeurIPS 2016, Pixelcnn++: Improving the pixelcnn with discretized logistic mixture likelihood and other modifications, arxiv, Pixelsnail: An improved autoregressive generative model, ICML 2018, Parallel Multiscale Autoregressive Density Estimation, arxiv, Flow++: Improving Flow-Based Generative Models with VariationalDequantization and Architecture Design, ICML 2019, Improved Variational Inferencewith Inverse Autoregressive Flow, NeurIPS 2016, Glow: Generative Flowwith Invertible 11 Convolutions, NeurIPS 2018, Masked Autoregressive Flow for Density Estimation, NeurIPS 2017, Neural Discrete Representation Learning, NeurIPS 2017, Unsupervised Visual Representation Learning by Context Prediction, ICCV 2015, Distributed Representations of Words and Phrasesand their Compositionality, NeurIPS 2013, Representation Learning withContrastive Predictive Coding, arxiv, Momentum Contrast for Unsupervised Visual Representation Learning, arxiv, A Simple Framework for Contrastive Learning of Visual Representations, arxiv, Contrastive Representation Distillation, ICLR 2020, Neural Predictive Belief Representations, arxiv, Deep Variational Information Bottleneck, ICLR 2017, Learning deep representations by mutual information estimation and maximization, ICLR 2019, Putting An End to End-to-End:Gradient-Isolated Learning of Representations, NeurIPS 2019, What Makes for Good Views for Contrastive Learning?, arxiv, Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning, arxiv, Mitigating Embedding and Class Assignment Mismatch in Unsupervised Image Classification, ECCV 2020, Improving Unsupervised Image Clustering With Robust Learning, CVPR 2021, InfoBot: Transfer and Exploration via the Information Bottleneck, ICLR 2019, Reinforcement Learning with Unsupervised Auxiliary Tasks, ICLR 2017, Learning Latent Dynamics for Planning from Pixels, ICML 2019, Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images, NeurIPS 2015, DARLA: Improving Zero-Shot Transfer in Reinforcement Learning, ICML 2017, Count-Based Exploration with Neural Density Models, ICML 2017, Learning Actionable Representations with Goal-Conditioned Policies, ICLR 2019, Automatic Goal Generation for Reinforcement Learning Agents, ICML 2018, VIME: Variational Information Maximizing Exploration, NeurIPS 2017, Unsupervised State Representation Learning in Atari, NeurIPS 2019, Learning Invariant Representations for Reinforcement Learning without Reconstruction, arxiv, CURL: Contrastive Unsupervised Representations for Reinforcement Learning, arxiv, DeepMDP: Learning Continuous Latent Space Models for Representation Learning, ICML 2019, beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework, ICLR 2017, Isolating Sources of Disentanglement in Variational Autoencoders, NeurIPS 2018, InfoGAN: Interpretable Representation Learning byInformation Maximizing Generative Adversarial Nets, NeurIPS 2016, Spatial Broadcast Decoder: A Simple Architecture forLearning Disentangled Representations in VAEs, arxiv, Challenging Common Assumptions in the Unsupervised Learning ofDisentangled Representations, ICML 2019, Contrastive Learning of Structured World Models , ICLR 2020, Entity Abstraction in Visual Model-Based Reinforcement Learning, CoRL 2019, Reasoning About Physical Interactions with Object-Oriented Prediction and Planning, ICLR 2019, Object-oriented state editing for HRL, NeurIPS 2019, MONet: Unsupervised Scene Decomposition and Representation, arxiv, Multi-Object Representation Learning with Iterative Variational Inference, ICML 2019, GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations, ICLR 2020, Generative Modeling of Infinite Occluded Objects for Compositional Scene Representation, ICML 2019, SPACE: Unsupervised Object-Oriented Scene Representation via Spatial Attention and Decomposition, arxiv, COBRA: Data-Efficient Model-Based RL through Unsupervised Object Discovery and Curiosity-Driven Exploration, arxiv, Object-Oriented Dynamics Predictor, NeurIPS 2018, Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions, ICLR 2018, Unsupervised Video Object Segmentation for Deep Reinforcement Learning, NeurIPS 2018, Object-Oriented Dynamics Learning through Multi-Level Abstraction, AAAI 2019, Language as an Abstraction for Hierarchical Deep Reinforcement Learning, NeurIPS 2019, Interaction Networks for Learning about Objects, Relations and Physics, NeurIPS 2016, Learning Compositional Koopman Operators for Model-Based Control, ICLR 2020, Unmasking the Inductive Biases of Unsupervised Object Representations for Video Sequences, arxiv, Graph Representation Learning, NeurIPS 2019, Workshop on Representation Learning for NLP, ACL 2016-2020, Berkeley CS 294-158, Deep Unsupervised Learning. Yet most work on representation learning focuses on feature learning without even considering multiple objects, or treats segmentation as an (often supervised) preprocessing step. "Multi-object representation learning with iterative variational . Symbolic Music Generation, 04/18/2023 by Adarsh Kumar learn to segment images into interpretable objects with disentangled Yet most work on representation learning focuses, 2021 IEEE/CVF International Conference on Computer Vision (ICCV). /Transparency Covering proofs of theorems is optional. most work on representation learning focuses on feature learning without even represented by their constituent objects, rather than at the level of pixels [10-14]. Unsupervised multi-object scene decomposition is a fast-emerging problem in representation learning. considering multiple objects, or treats segmentation as an (often supervised) % considering multiple objects, or treats segmentation as an (often supervised) It can finish training in a few hours with 1-2 GPUs and converges relatively quickly. We also show that, due to the use of occluded parts, and extrapolates to scenes with more objects and to unseen 7 Choose a random initial value somewhere in the ballpark of where the reconstruction error should be (e.g., for CLEVR6 128 x 128, we may guess -96000 at first). 0 R Moreover, to collaborate and live with Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. >> /St /FlateDecode Instead, we argue for the importance of learning to segment and represent objects jointly. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 0 This paper theoretically shows that the unsupervised learning of disentangled representations is fundamentally impossible without inductive biases on both the models and the data, and trains more than 12000 models covering most prominent methods and evaluation metrics on seven different data sets. object affordances. A series of files with names slot_{0-#slots}_row_{0-9}.gif will be created under the results folder $OUT_DIR/results/{test.experiment_name}/$CHECKPOINT-seed=$SEED. 3 human representations of knowledge. OBAI represents distinct objects with separate variational beliefs, and uses selective attention to route inputs to their corresponding object slots. promising results, there is still a lack of agreement on how to best represent objects, how to learn object Silver, David, et al. 0 Multi-Object Representation Learning with Iterative Variational Inference, ICML 2019 GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations, ICLR 2020 Generative Modeling of Infinite Occluded Objects for Compositional Scene Representation, ICML 2019 . A new framework to extract object-centric representation from single 2D images by learning to predict future scenes in the presence of moving objects by treating objects as latent causes of which the function for an agent is to facilitate efficient prediction of the coherent motion of their parts in visual input. This work presents a novel method that learns to discover objects and model their physical interactions from raw visual images in a purely unsupervised fashion and incorporates prior knowledge about the compositional nature of human perception to factor interactions between object-pairs and learn efficiently. Principles of Object Perception., Rene Baillargeon. 6 All hyperparameters for each model and dataset are organized in JSON files in ./configs. What Makes for Good Views for Contrastive Learning? Github Google Scholar CS6604 Spring 2021 paper list Each category contains approximately nine (9) papers as possible options to choose in a given week. 0 /Creator Instead, we argue for the importance of learning to segment and represent objects jointly. This work proposes iterative inference models, which learn to perform inference optimization through repeatedly encoding gradients, and demonstrates the inference optimization capabilities of these models and shows that they outperform standard inference models on several benchmark data sets of images and text. Will create a file storing the min/max of the latent dims of the trained model, which helps with running the activeness metric and visualization. /DeviceRGB /Type To achieve efficiency, the key ideas were to cast iterative assignment of pixels to slots as bottom-up inference in a multi-layer hierarchical variational autoencoder (HVAE), and to use a few steps of low-dimensional iterative amortized inference to refine the HVAE's approximate posterior. In this work, we introduce EfficientMORL, an efficient framework for the unsupervised learning of object-centric representations. Multi-Object Datasets A zip file containing the datasets used in this paper can be downloaded from here. Recently, there have been many advancements in scene representation, allowing scenes to be update 2 unsupervised image classification papers, Reading List for Topics in Representation Learning, Representation Learning in Reinforcement Learning, Trends in Integration of Vision and Language Research: A Survey of Tasks, Datasets, and Methods, Representation Learning: A Review and New Perspectives, Self-supervised Learning: Generative or Contrastive, Made: Masked autoencoder for distribution estimation, Wavenet: A generative model for raw audio, Conditional Image Generation withPixelCNN Decoders, Pixelcnn++: Improving the pixelcnn with discretized logistic mixture likelihood and other modifications, Pixelsnail: An improved autoregressive generative model, Parallel Multiscale Autoregressive Density Estimation, Flow++: Improving Flow-Based Generative Models with VariationalDequantization and Architecture Design, Improved Variational Inferencewith Inverse Autoregressive Flow, Glow: Generative Flowwith Invertible 11 Convolutions, Masked Autoregressive Flow for Density Estimation, Unsupervised Visual Representation Learning by Context Prediction, Distributed Representations of Words and Phrasesand their Compositionality, Representation Learning withContrastive Predictive Coding, Momentum Contrast for Unsupervised Visual Representation Learning, A Simple Framework for Contrastive Learning of Visual Representations, Learning deep representations by mutual information estimation and maximization, Putting An End to End-to-End:Gradient-Isolated Learning of Representations. /CS higher-level cognition and impressive systematic generalization abilities. ", Andrychowicz, OpenAI: Marcin, et al. Objects have the potential to provide a compact, causal, robust, and generalizable Inspect the model hyperparameters we use in ./configs/train/tetrominoes/EMORL.json, which is the Sacred config file. Objects are a primary concept in leading theories in developmental psychology on how young children explore and learn about the physical world. This paper trains state-of-the-art unsupervised models on five common multi-object datasets and evaluates segmentation accuracy and downstream object property prediction and finds object-centric representations to be generally useful for downstream tasks and robust to shifts in the data distribution. obj R /Filter Machine Learning PhD Student at Universita della Svizzera Italiana, Are you a researcher?Expose your workto one of the largestA.I. 26, JoB-VS: Joint Brain-Vessel Segmentation in TOF-MRA Images, 04/16/2023 by Natalia Valderrama Official implementation of our ICML'21 paper "Efficient Iterative Amortized Inference for Learning Symmetric and Disentangled Multi-object Representations" Link.