23 research outputs found
Disentangling Factors of Variation with Cycle-Consistent Variational Auto-Encoders
Generative models that learn disentangled representations for different
factors of variation in an image can be very useful for targeted data
augmentation. By sampling from the disentangled latent subspace of interest, we
can efficiently generate new data necessary for a particular task. Learning
disentangled representations is a challenging problem, especially when certain
factors of variation are difficult to label. In this paper, we introduce a
novel architecture that disentangles the latent space into two complementary
subspaces by using only weak supervision in form of pairwise similarity labels.
Inspired by the recent success of cycle-consistent adversarial architectures,
we use cycle-consistency in a variational auto-encoder framework. Our
non-adversarial approach is in contrast with the recent works that combine
adversarial training with auto-encoders to disentangle representations. We show
compelling results of disentangled latent subspaces on three datasets and
compare with recent works that leverage adversarial training
It's LeVAsa not LevioSA! Latent Encodings for Valence-Arousal Structure Alignment
In recent years, great strides have been made in the field of affective
computing. Several models have been developed to represent and quantify
emotions. Two popular ones include (i) categorical models which represent
emotions as discrete labels, and (ii) dimensional models which represent
emotions in a Valence-Arousal (VA) circumplex domain. However, there is no
standard for annotation mapping between the two labelling methods. We build a
novel algorithm for mapping categorical and dimensional model labels using
annotation transfer across affective facial image datasets. Further, we utilize
the transferred annotations to learn rich and interpretable data
representations using a variational autoencoder (VAE). We present "LeVAsa", a
VAE model that learns implicit structure by aligning the latent space with the
VA space. We evaluate the efficacy of LeVAsa by comparing performance with the
Vanilla VAE using quantitative and qualitative analysis on two benchmark
affective image datasets. Our results reveal that LeVAsa achieves high
latent-circumplex alignment which leads to improved downstream categorical
emotion prediction. The work also demonstrates the trade-off between degree of
alignment and quality of reconstructions.Comment: 5 pages, 4 figures and 3 table
Automaton Distillation: Neuro-Symbolic Transfer Learning for Deep Reinforcement Learning
Reinforcement learning (RL) is a powerful tool for finding optimal policies
in sequential decision processes. However, deep RL methods suffer from two
weaknesses: collecting the amount of agent experience required for practical RL
problems is prohibitively expensive, and the learned policies exhibit poor
generalization on tasks outside of the training distribution. To mitigate these
issues, we introduce automaton distillation, a form of neuro-symbolic transfer
learning in which Q-value estimates from a teacher are distilled into a
low-dimensional representation in the form of an automaton. We then propose two
methods for generating Q-value estimates: static transfer, which reasons over
an abstract Markov Decision Process constructed based on prior knowledge, and
dynamic transfer, where symbolic information is extracted from a teacher Deep
Q-Network (DQN). The resulting Q-value estimates from either method are used to
bootstrap learning in the target environment via a modified DQN loss function.
We list several failure modes of existing automaton-based transfer methods and
demonstrate that both static and dynamic automaton distillation decrease the
time required to find optimal policies for various decision tasks