26 research outputs found
Multi-Adversarial Domain Adaptation
Recent advances in deep domain adaptation reveal that adversarial learning
can be embedded into deep networks to learn transferable features that reduce
distribution discrepancy between the source and target domains. Existing domain
adversarial adaptation methods based on single domain discriminator only align
the source and target data distributions without exploiting the complex
multimode structures. In this paper, we present a multi-adversarial domain
adaptation (MADA) approach, which captures multimode structures to enable
fine-grained alignment of different data distributions based on multiple domain
discriminators. The adaptation can be achieved by stochastic gradient descent
with the gradients computed by back-propagation in linear-time. Empirical
evidence demonstrates that the proposed model outperforms state of the art
methods on standard domain adaptation datasets.Comment: AAAI 2018 Oral. arXiv admin note: substantial text overlap with
arXiv:1705.10667, arXiv:1707.0790
Partial Transfer Learning with Selective Adversarial Networks
Adversarial learning has been successfully embedded into deep networks to
learn transferable features, which reduce distribution discrepancy between the
source and target domains. Existing domain adversarial networks assume fully
shared label space across domains. In the presence of big data, there is strong
motivation of transferring both classification and representation models from
existing big domains to unknown small domains. This paper introduces partial
transfer learning, which relaxes the shared label space assumption to that the
target label space is only a subspace of the source label space. Previous
methods typically match the whole source domain to the target domain, which are
prone to negative transfer for the partial transfer problem. We present
Selective Adversarial Network (SAN), which simultaneously circumvents negative
transfer by selecting out the outlier source classes and promotes positive
transfer by maximally matching the data distributions in the shared label
space. Experiments demonstrate that our models exceed state-of-the-art results
for partial transfer learning tasks on several benchmark datasets
AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows
Given datasets from multiple domains, a key challenge is to efficiently
exploit these data sources for modeling a target domain. Variants of this
problem have been studied in many contexts, such as cross-domain translation
and domain adaptation. We propose AlignFlow, a generative modeling framework
that models each domain via a normalizing flow. The use of normalizing flows
allows for a) flexibility in specifying learning objectives via adversarial
training, maximum likelihood estimation, or a hybrid of the two methods; and b)
learning and exact inference of a shared representation in the latent space of
the generative model. We derive a uniform set of conditions under which
AlignFlow is marginally-consistent for the different learning objectives.
Furthermore, we show that AlignFlow guarantees exact cycle consistency in
mapping datapoints from a source domain to target and back to the source
domain. Empirically, AlignFlow outperforms relevant baselines on image-to-image
translation and unsupervised domain adaptation and can be used to
simultaneously interpolate across the various domains using the learned
representation.Comment: AAAI 202
Hub-Pathway: Transfer Learning from A Hub of Pre-trained Models
Transfer learning aims to leverage knowledge from pre-trained models to
benefit the target task. Prior transfer learning work mainly transfers from a
single model. However, with the emergence of deep models pre-trained from
different resources, model hubs consisting of diverse models with various
architectures, pre-trained datasets and learning paradigms are available.
Directly applying single-model transfer learning methods to each model wastes
the abundant knowledge of the model hub and suffers from high computational
cost. In this paper, we propose a Hub-Pathway framework to enable knowledge
transfer from a model hub. The framework generates data-dependent pathway
weights, based on which we assign the pathway routes at the input level to
decide which pre-trained models are activated and passed through, and then set
the pathway aggregation at the output level to aggregate the knowledge from
different models to make predictions. The proposed framework can be trained
end-to-end with the target task-specific loss, where it learns to explore
better pathway configurations and exploit the knowledge in pre-trained models
for each target datum. We utilize a noisy pathway generator and design an
exploration loss to further explore different pathways throughout the model
hub. To fully exploit the knowledge in pre-trained models, each model is
further trained by specific data that activate it, which ensures its
performance and enhances knowledge transfer. Experiment results on computer
vision and reinforcement learning tasks demonstrate that the proposed
Hub-Pathway framework achieves the state-of-the-art performance for model hub
transfer learning.Comment: Accepted by NeurIPS 202
Masked Imitation Learning: Discovering Environment-Invariant Modalities in Multimodal Demonstrations
Multimodal demonstrations provide robots with an abundance of information to
make sense of the world. However, such abundance may not always lead to good
performance when it comes to learning sensorimotor control policies from human
demonstrations.
Extraneous data modalities can lead to state over-specification, where the
state contains modalities that are not only useless for decision-making but
also can change data distribution across environments. State over-specification
leads to issues such as the learned policy not generalizing outside of the
training data distribution.
In this work, we propose Masked Imitation Learning (MIL) to address state
over-specification by selectively using informative modalities. Specifically,
we design a masked policy network with a binary mask to block certain
modalities. We develop a bi-level optimization algorithm that learns this mask
to accurately filter over-specified modalities. We demonstrate empirically that
MIL outperforms baseline algorithms in simulated domains including MuJoCo and a
robot arm environment using the Robomimic dataset, and effectively recovers the
environment-invariant modalities on a multimodal dataset collected on a real
robot. Our project website presents supplemental details and videos of our
results at: https://tinyurl.com/masked-ilComment: 13 page