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Found 20 papers, 13 papers with code
Scaling Instructable Agents Across Many Simulated Worlds
Building embodied AI systems that can follow arbitrary language instructions in any 3D environment is a key challenge for creating general AI.
Evaluating VLMs for Score-Based, Multi-Probe Annotation of 3D Objects
Unlabeled 3D objects present an opportunity to leverage pretrained vision language models (VLMs) on a range of annotation tasks -- from describing object semantics to physical properties.
SODA: Bottleneck Diffusion Models for Representation Learning
We introduce SODA, a self-supervised diffusion model, designed for representation learning.
Constellation: Learning relational abstractions over objects for compositional imagination
Learning structured representations of visual scenes is currently a major bottleneck to bridging perception with reasoning.
SIMONe: View-Invariant, Temporally-Abstracted Object Representations via Unsupervised Video Decomposition
Leveraging the shared structure that exists across different scenes, our model learns to infer two sets of latent representations from RGB video input alone: a set of "object" latents, corresponding to the time-invariant, object-level contents of the scene, as well as a set of "frame" latents, corresponding to global time-varying elements such as viewpoint.
Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agents
There has been rapidly growing interest in meta-learning as a method for increasing the flexibility and sample efficiency of reinforcement learning.
AlignNet: Unsupervised Entity Alignment
Recently developed deep learning models are able to learn to segment scenes into component objects without supervision.
Unsupervised Model Selection for Variational Disentangled Representation Learning
Disentangled representations have recently been shown to improve fairness, data efficiency and generalisation in simple supervised and reinforcement learning tasks.
COBRA: Data-Efficient Model-Based RL through Unsupervised Object Discovery and Curiosity-Driven Exploration
Data efficiency and robustness to task-irrelevant perturbations are long-standing challenges for deep reinforcement learning algorithms.
Spatial Broadcast Decoder: A Simple Architecture for Disentangled Representations in VAEs
We present a neural rendering architecture that helps variational autoencoders (VAEs) learn disentangled representations.
Multi-Object Representation Learning with Iterative Variational Inference
Human perception is structured around objects which form the basis for our higher-level cognition and impressive systematic generalization abilities.
MONet: Unsupervised Scene Decomposition and Representation
The ability to decompose scenes in terms of abstract building blocks is crucial for general intelligence.
Spatial Broadcast Decoder: A Simple Architecture for Learning Disentangled Representations in VAEs
We present a simple neural rendering architecture that helps variational autoencoders (VAEs) learn disentangled representations.
Towards a Definition of Disentangled Representations
Here we propose that a principled solution to characterising disentangled representations can be found by focusing on the transformation properties of the world.
Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies
Intelligent behaviour in the real-world requires the ability to acquire new knowledge from an ongoing sequence of experiences while preserving and reusing past knowledge.
Understanding disentangling in $β$-VAE
We present new intuitions and theoretical assessments of the emergence of disentangled representation in variational autoencoders.
DARLA: Improving Zero-Shot Transfer in Reinforcement Learning
Domain adaptation is an important open problem in deep reinforcement learning (RL).
SCAN: Learning Hierarchical Compositional Visual Concepts
SCAN learns concepts through fast symbol association, grounding them in disentangled visual primitives that are discovered in an unsupervised manner.
beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework
Learning an interpretable factorised representation of the independent data generative factors of the world without supervision is an important precursor for the development of artificial intelligence that is able to learn and reason in the same way that humans do.
Early Visual Concept Learning with Unsupervised Deep Learning
Automated discovery of early visual concepts from raw image data is a major open challenge in AI research.
