13,683 research outputs found
Invariant Representations without Adversarial Training
Representations of data that are invariant to changes in specified factors
are useful for a wide range of problems: removing potential biases in
prediction problems, controlling the effects of covariates, and disentangling
meaningful factors of variation. Unfortunately, learning representations that
exhibit invariance to arbitrary nuisance factors yet remain useful for other
tasks is challenging. Existing approaches cast the trade-off between task
performance and invariance in an adversarial way, using an iterative minimax
optimization. We show that adversarial training is unnecessary and sometimes
counter-productive; we instead cast invariant representation learning as a
single information-theoretic objective that can be directly optimized. We
demonstrate that this approach matches or exceeds performance of
state-of-the-art adversarial approaches for learning fair representations and
for generative modeling with controllable transformations.Comment: NeurIPS 2018, with correction
AutoBayes: Automated Bayesian Graph Exploration for Nuisance-Robust Inference
Learning data representations that capture task-related features, but are
invariant to nuisance variations remains a key challenge in machine learning.
We introduce an automated Bayesian inference framework, called AutoBayes, that
explores different graphical models linking classifier, encoder, decoder,
estimator and adversarial network blocks to optimize nuisance-invariant machine
learning pipelines. AutoBayes also enables learning disentangled
representations, where the latent variable is split into multiple pieces to
impose various relationships with the nuisance variation and task labels. We
benchmark the framework on several public datasets, and provide analysis of its
capability for subject-transfer learning with/without variational modeling and
adversarial training. We demonstrate a significant performance improvement with
ensemble learning across explored graphical models.Comment: 24 pages, 11 figures, under review in ICLR202
To Reverse the Gradient or Not: An Empirical Comparison of Adversarial and Multi-task Learning in Speech Recognition
Transcribed datasets typically contain speaker identity for each instance in
the data. We investigate two ways to incorporate this information during
training: Multi-Task Learning and Adversarial Learning. In multi-task learning,
the goal is speaker prediction; we expect a performance improvement with this
joint training if the two tasks of speech recognition and speaker recognition
share a common set of underlying features. In contrast, adversarial learning is
a means to learn representations invariant to the speaker. We then expect
better performance if this learnt invariance helps generalizing to new
speakers. While the two approaches seem natural in the context of speech
recognition, they are incompatible because they correspond to opposite
gradients back-propagated to the model. In order to better understand the
effect of these approaches in terms of error rates, we compare both strategies
in controlled settings. Moreover, we explore the use of additional
untranscribed data in a semi-supervised, adversarial learning manner to improve
error rates. Our results show that deep models trained on big datasets already
develop invariant representations to speakers without any auxiliary loss. When
considering adversarial learning and multi-task learning, the impact on the
acoustic model seems minor. However, models trained in a semi-supervised manner
can improve error-rates
Drop to Adapt: Learning Discriminative Features for Unsupervised Domain Adaptation
Recent works on domain adaptation exploit adversarial training to obtain
domain-invariant feature representations from the joint learning of feature
extractor and domain discriminator networks. However, domain adversarial
methods render suboptimal performances since they attempt to match the
distributions among the domains without considering the task at hand. We
propose Drop to Adapt (DTA), which leverages adversarial dropout to learn
strongly discriminative features by enforcing the cluster assumption.
Accordingly, we design objective functions to support robust domain adaptation.
We demonstrate efficacy of the proposed method on various experiments and
achieve consistent improvements in both image classification and semantic
segmentation tasks. Our source code is available at
https://github.com/postBG/DTA.pytorch.Comment: ICCV 201
Transfer Learning in Brain-Computer Interfaces with Adversarial Variational Autoencoders
We introduce adversarial neural networks for representation learning as a
novel approach to transfer learning in brain-computer interfaces (BCIs). The
proposed approach aims to learn subject-invariant representations by
simultaneously training a conditional variational autoencoder (cVAE) and an
adversarial network. We use shallow convolutional architectures to realize the
cVAE, and the learned encoder is transferred to extract subject-invariant
features from unseen BCI users' data for decoding. We demonstrate a
proof-of-concept of our approach based on analyses of electroencephalographic
(EEG) data recorded during a motor imagery BCI experiment.Comment: 9th International IEEE EMBS Conference on Neural Engineering (NER'19
Variational Interaction Information Maximization for Cross-domain Disentanglement
Cross-domain disentanglement is the problem of learning representations
partitioned into domain-invariant and domain-specific representations, which is
a key to successful domain transfer or measuring semantic distance between two
domains. Grounded in information theory, we cast the simultaneous learning of
domain-invariant and domain-specific representations as a joint objective of
multiple information constraints, which does not require adversarial training
or gradient reversal layers. We derive a tractable bound of the objective and
propose a generative model named Interaction Information Auto-Encoder (IIAE).
Our approach reveals insights on the desirable representation for cross-domain
disentanglement and its connection to Variational Auto-Encoder (VAE). We
demonstrate the validity of our model in the image-to-image translation and the
cross-domain retrieval tasks. We further show that our model achieves the
state-of-the-art performance in the zero-shot sketch based image retrieval
task, even without external knowledge. Our implementation is publicly available
at: https://github.com/gr8joo/IIAEComment: Published at NeurIPS 202
Excessive Invariance Causes Adversarial Vulnerability
Despite their impressive performance, deep neural networks exhibit striking
failures on out-of-distribution inputs. One core idea of adversarial example
research is to reveal neural network errors under such distribution shifts. We
decompose these errors into two complementary sources: sensitivity and
invariance. We show deep networks are not only too sensitive to task-irrelevant
changes of their input, as is well-known from epsilon-adversarial examples, but
are also too invariant to a wide range of task-relevant changes, thus making
vast regions in input space vulnerable to adversarial attacks. We show such
excessive invariance occurs across various tasks and architecture types. On
MNIST and ImageNet one can manipulate the class-specific content of almost any
image without changing the hidden activations. We identify an insufficiency of
the standard cross-entropy loss as a reason for these failures. Further, we
extend this objective based on an information-theoretic analysis so it
encourages the model to consider all task-dependent features in its decision.
This provides the first approach tailored explicitly to overcome excessive
invariance and resulting vulnerabilities
Diverse Image-to-Image Translation via Disentangled Representations
Image-to-image translation aims to learn the mapping between two visual
domains. There are two main challenges for many applications: 1) the lack of
aligned training pairs and 2) multiple possible outputs from a single input
image. In this work, we present an approach based on disentangled
representation for producing diverse outputs without paired training images. To
achieve diversity, we propose to embed images onto two spaces: a
domain-invariant content space capturing shared information across domains and
a domain-specific attribute space. Our model takes the encoded content features
extracted from a given input and the attribute vectors sampled from the
attribute space to produce diverse outputs at test time. To handle unpaired
training data, we introduce a novel cross-cycle consistency loss based on
disentangled representations. Qualitative results show that our model can
generate diverse and realistic images on a wide range of tasks without paired
training data. For quantitative comparisons, we measure realism with user study
and diversity with a perceptual distance metric. We apply the proposed model to
domain adaptation and show competitive performance when compared to the
state-of-the-art on the MNIST-M and the LineMod datasets.Comment: ECCV 2018 (Oral). Project page: http://vllab.ucmerced.edu/hylee/DRIT/
Code: https://github.com/HsinYingLee/DRIT
Unified Adversarial Invariance
We present a unified invariance framework for supervised neural networks that
can induce independence to nuisance factors of data without using any nuisance
annotations, but can additionally use labeled information about biasing factors
to force their removal from the latent embedding for making fair predictions.
Invariance to nuisance is achieved by learning a split representation of data
through competitive training between the prediction task and a reconstruction
task coupled with disentanglement, whereas that to biasing factors is brought
about by penalizing the network if the latent embedding contains any
information about them. We describe an adversarial instantiation of this
framework and provide analysis of its working. Our model outperforms previous
works at inducing invariance to nuisance factors without using any labeled
information about such variables, and achieves state-of-the-art performance at
learning independence to biasing factors in fairness settings.Comment: In submission to T-PAMI. Some results updated. arXiv admin note:
substantial text overlap with arXiv:1809.1008
Adversarial Deep Learning in EEG Biometrics
Deep learning methods for person identification based on
electroencephalographic (EEG) brain activity encounters the problem of
exploiting the temporally correlated structures or recording session specific
variability within EEG. Furthermore, recent methods have mostly trained and
evaluated based on single session EEG data. We address this problem from an
invariant representation learning perspective. We propose an adversarial
inference approach to extend such deep learning models to learn
session-invariant person-discriminative representations that can provide
robustness in terms of longitudinal usability. Using adversarial learning
within a deep convolutional network, we empirically assess and show
improvements with our approach based on longitudinally collected EEG data for
person identification from half-second EEG epochs.Comment: Accepted for publication by IEEE Signal Processing Letter
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