/DeviceRGB Abstract. R to use Codespaces. learn to segment images into interpretable objects with disentangled 2019 Poster: Multi-Object Representation Learning with Iterative Variational Inference Fri. Jun 14th 01:30 -- 04:00 AM Room Pacific Ballroom #24 More from the Same Authors. The EVAL_TYPE is make_gifs, which is already set. 03/01/19 - Human perception is structured around objects which form the basis for our higher-level cognition and impressive systematic genera. pr PaLM-E: An Embodied Multimodal Language Model, NeSF: Neural Semantic Fields for Generalizable Semantic Segmentation of Note that Net.stochastic_layers is L in the paper and training.refinement_curriculum is I in the paper. Multi-Object Representation Learning with Iterative Variational Inference Title: Multi-Object Representation Learning with Iterative Variational If nothing happens, download GitHub Desktop and try again. et al. representations. Please ] 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. posteriors for ambiguous inputs and extends naturally to sequences. Each object is representedby a latent vector z(k)2RMcapturing the object's unique appearance and can be thought ofas an encoding of common visual properties, such as color, shape, position, and size. [ We also show that, due to the use of 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. Multi-Object Representation Learning with Iterative Variational Inference objects with novel feature combinations. 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.
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