9 research outputs found
GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations
Generative latent-variable models are emerging as promising tools in robotics
and reinforcement learning. Yet, even though tasks in these domains typically
involve distinct objects, most state-of-the-art generative models do not
explicitly capture the compositional nature of visual scenes. Two recent
exceptions, MONet and IODINE, decompose scenes into objects in an unsupervised
fashion. Their underlying generative processes, however, do not account for
component interactions. Hence, neither of them allows for principled sampling
of novel scenes. Here we present GENESIS, the first object-centric generative
model of 3D visual scenes capable of both decomposing and generating scenes by
capturing relationships between scene components. GENESIS parameterises a
spatial GMM over images which is decoded from a set of object-centric latent
variables that are either inferred sequentially in an amortised fashion or
sampled from an autoregressive prior. We train GENESIS on several publicly
available datasets and evaluate its performance on scene generation,
decomposition, and semi-supervised learning.Comment: Published at the International Conference on Learning Representations
(ICLR) 202
Scrutinizing and De-Biasing Intuitive Physics with Neural Stethoscopes
Visually predicting the stability of block towers is a popular task in the
domain of intuitive physics. While previous work focusses on prediction
accuracy, a one-dimensional performance measure, we provide a broader analysis
of the learned physical understanding of the final model and how the learning
process can be guided. To this end, we introduce neural stethoscopes as a
general purpose framework for quantifying the degree of importance of specific
factors of influence in deep neural networks as well as for actively promoting
and suppressing information as appropriate. In doing so, we unify concepts from
multitask learning as well as training with auxiliary and adversarial losses.
We apply neural stethoscopes to analyse the state-of-the-art neural network for
stability prediction. We show that the baseline model is susceptible to being
misled by incorrect visual cues. This leads to a performance breakdown to the
level of random guessing when training on scenarios where visual cues are
inversely correlated with stability. Using stethoscopes to promote meaningful
feature extraction increases performance from 51% to 90% prediction accuracy.
Conversely, training on an easy dataset where visual cues are positively
correlated with stability, the baseline model learns a bias leading to poor
performance on a harder dataset. Using an adversarial stethoscope, the network
is successfully de-biased, leading to a performance increase from 66% to 88%
MetaFun: Meta-Learning with Iterative Functional Updates
We develop a functional encoder-decoder approach to supervised meta-learning,
where labeled data is encoded into an infinite-dimensional functional
representation rather than a finite-dimensional one. Furthermore, rather than
directly producing the representation, we learn a neural update rule resembling
functional gradient descent which iteratively improves the representation. The
final representation is used to condition the decoder to make predictions on
unlabeled data. Our approach is the first to demonstrates the success of
encoder-decoder style meta-learning methods like conditional neural processes
on large-scale few-shot classification benchmarks such as miniImageNet and
tieredImageNet, where it achieves state-of-the-art performance
Revisiting Reweighted Wake-Sleep for Models with Stochastic Control Flow
Stochastic control-flow models (SCFMs) are a class of generative models that
involve branching on choices from discrete random variables. Amortized
gradient-based learning of SCFMs is challenging as most approaches targeting
discrete variables rely on their continuous relaxations---which can be
intractable in SCFMs, as branching on relaxations requires evaluating all
(exponentially many) branching paths. Tractable alternatives mainly combine
REINFORCE with complex control-variate schemes to improve the variance of naive
estimators. Here, we revisit the reweighted wake-sleep (RWS) (Bornschein and
Bengio, 2015) algorithm, and through extensive evaluations, show that it
outperforms current state-of-the-art methods in learning SCFMs. Further, in
contrast to the importance weighted autoencoder, we observe that RWS learns
better models and inference networks with increasing numbers of particles. Our
results suggest that RWS is a competitive, often preferable, alternative for
learning SCFMs.Comment: Tuan Anh Le and Adam R. Kosiorek contributed equally; accepted to
Uncertainty in Artificial Intelligence 201