255,978 research outputs found
Co-regularized Alignment for Unsupervised Domain Adaptation
Deep neural networks, trained with large amount of labeled data, can fail to
generalize well when tested with examples from a \emph{target domain} whose
distribution differs from the training data distribution, referred as the
\emph{source domain}. It can be expensive or even infeasible to obtain required
amount of labeled data in all possible domains. Unsupervised domain adaptation
sets out to address this problem, aiming to learn a good predictive model for
the target domain using labeled examples from the source domain but only
unlabeled examples from the target domain. Domain alignment approaches this
problem by matching the source and target feature distributions, and has been
used as a key component in many state-of-the-art domain adaptation methods.
However, matching the marginal feature distributions does not guarantee that
the corresponding class conditional distributions will be aligned across the
two domains. We propose co-regularized domain alignment for unsupervised domain
adaptation, which constructs multiple diverse feature spaces and aligns source
and target distributions in each of them individually, while encouraging that
alignments agree with each other with regard to the class predictions on the
unlabeled target examples. The proposed method is generic and can be used to
improve any domain adaptation method which uses domain alignment. We
instantiate it in the context of a recent state-of-the-art method and observe
that it provides significant performance improvements on several domain
adaptation benchmarks.Comment: NIPS 2018 accepted versio
Co-Training for Unsupervised Domain Adaptation of Semantic Segmentation Models
Semantic image segmentation is a central and challenging task in autonomous
driving, addressed by training deep models. Since this training draws to a
curse of human-based image labeling, using synthetic images with automatically
generated labels together with unlabeled real-world images is a promising
alternative. This implies to address an unsupervised domain adaptation (UDA)
problem. In this paper, we propose a new co-training procedure for
synth-to-real UDA of semantic segmentation models. It consists of a
self-training stage, which provides two domain-adapted models, and a model
collaboration loop for the mutual improvement of these two models. These models
are then used to provide the final semantic segmentation labels (pseudo-labels)
for the real-world images. The overall procedure treats the deep models as
black boxes and drives their collaboration at the level of pseudo-labeled
target images, i.e., neither modifying loss functions is required, nor explicit
feature alignment. We test our proposal on standard synthetic and real-world
datasets for on-board semantic segmentation. Our procedure shows improvements
ranging from ~13 to ~26 mIoU points over baselines, so establishing new
state-of-the-art results
Rethinking the Role of Pre-Trained Networks in Source-Free Domain Adaptation
Source-free domain adaptation (SFDA) aims to adapt a source model trained on
a fully-labeled source domain to an unlabeled target domain. Large-data
pre-trained networks are used to initialize source models during source
training, and subsequently discarded. However, source training can cause the
model to overfit to source data distribution and lose applicable target domain
knowledge. We propose to integrate the pre-trained network into the target
adaptation process as it has diversified features important for generalization
and provides an alternate view of features and classification decisions
different from the source model. We propose to distil useful target domain
information through a co-learning strategy to improve target pseudolabel
quality for finetuning the source model. Evaluation on 4 benchmark datasets
show that our proposed strategy improves adaptation performance and can be
successfully integrated with existing SFDA methods. Leveraging modern
pre-trained networks that have stronger representation learning ability in the
co-learning strategy further boosts performance.Comment: Accepted to ICCV 202
StereoFlowGAN: Co-training for Stereo and Flow with Unsupervised Domain Adaptation
We introduce a novel training strategy for stereo matching and optical flow
estimation that utilizes image-to-image translation between synthetic and real
image domains. Our approach enables the training of models that excel in real
image scenarios while relying solely on ground-truth information from synthetic
images. To facilitate task-agnostic domain adaptation and the training of
task-specific components, we introduce a bidirectional feature warping module
that handles both left-right and forward-backward directions. Experimental
results show competitive performance over previous domain translation-based
methods, which substantiate the efficacy of our proposed framework, effectively
leveraging the benefits of unsupervised domain adaptation, stereo matching, and
optical flow estimation.Comment: Accepted by BMVC 202
Learning with Single View Co-training and Marginalized Dropout
The generalization properties of most existing machine learning techniques are predicated on the assumptions that 1) a sufficiently large quantity of training data is available; 2) the training and testing data come from some common distribution. Although these assumptions are often met in practice, there are also many scenarios in which training data from the relevant distribution is insufficient. We focus on making use of additional data, which is readily available or can be obtained easily but comes from a different distribution than the testing data, to aid learning.
We present five learning scenarios, depending on how the distribution we used to sample the additional training data differs from the testing distribution: 1) learning with weak supervision; 2) domain adaptation; 3) learning from multiple domains; 4) learning from corrupted data; 5) learning with partial supervision.
We introduce two strategies and manifest them in five ways to cope with the difference between the training and testing distribution. The first strategy, which gives rise to Pseudo Multi-view Co-training: PMC) and Co-training for Domain Adaptation: CODA), is inspired by the co-training algorithm for multi-view data. PMC generalizes co-training to the more common single view data and allows us to learn from weakly labeled data retrieved free from the web. CODA integrates PMC with an another feature selection component to address the feature incompatibility between domains for domain adaptation. PMC and CODA are evaluated on a variety of real datasets, and both yield record performance.
The second strategy marginalized dropout leads to marginalized Stacked Denoising Autoencoders: mSDA), Marginalized Corrupted Features: MCF) and FastTag: FastTag). mSDA diminishes the difference between distributions associated with different domains by learning a new representation through marginalized corruption and reconstruciton. MCF learns from a known distribution which is created by corrupting a small set of training data, and improves robustness of learned classifiers by training on ``infinitely\u27\u27 many data sampled from the distribution. FastTag applies marginalized dropout to the output of partially labeled data to recover missing labels for multi-label tasks. These three algorithms not only achieve the state-of-art performance in various tasks, but also deliver orders of magnitude speed up at training and testing comparing to competing algorithms
Cross Language Text Classification via Subspace Co-Regularized Multi-View Learning
In many multilingual text classification problems, the documents in different
languages often share the same set of categories. To reduce the labeling cost
of training a classification model for each individual language, it is
important to transfer the label knowledge gained from one language to another
language by conducting cross language classification. In this paper we develop
a novel subspace co-regularized multi-view learning method for cross language
text classification. This method is built on parallel corpora produced by
machine translation. It jointly minimizes the training error of each classifier
in each language while penalizing the distance between the subspace
representations of parallel documents. Our empirical study on a large set of
cross language text classification tasks shows the proposed method consistently
outperforms a number of inductive methods, domain adaptation methods, and
multi-view learning methods.Comment: Appears in Proceedings of the 29th International Conference on
Machine Learning (ICML 2012
Privacy-Preserving Models for Legal Natural Language Processing
Pre-training large transformer models with in-domain data improves domain
adaptation and helps gain performance on the domain-specific downstream tasks.
However, sharing models pre-trained on potentially sensitive data is prone to
adversarial privacy attacks. In this paper, we asked to which extent we can
guarantee privacy of pre-training data and, at the same time, achieve better
downstream performance on legal tasks without the need of additional labeled
data. We extensively experiment with scalable self-supervised learning of
transformer models under the formal paradigm of differential privacy and show
that under specific training configurations we can improve downstream
performance without sacrifying privacy protection for the in-domain data. Our
main contribution is utilizing differential privacy for large-scale
pre-training of transformer language models in the legal NLP domain, which, to
the best of our knowledge, has not been addressed before.Comment: Camera ready, to appear at the Natural Legal Language Processing
Workshop 2022 co-located with EMNL
Variational Counterfactual Prediction under Runtime Domain Corruption
To date, various neural methods have been proposed for causal effect
estimation based on observational data, where a default assumption is the same
distribution and availability of variables at both training and inference
(i.e., runtime) stages. However, distribution shift (i.e., domain shift) could
happen during runtime, and bigger challenges arise from the impaired
accessibility of variables. This is commonly caused by increasing privacy and
ethical concerns, which can make arbitrary variables unavailable in the entire
runtime data and imputation impractical. We term the co-occurrence of domain
shift and inaccessible variables runtime domain corruption, which seriously
impairs the generalizability of a trained counterfactual predictor. To counter
runtime domain corruption, we subsume counterfactual prediction under the
notion of domain adaptation. Specifically, we upper-bound the error w.r.t. the
target domain (i.e., runtime covariates) by the sum of source domain error and
inter-domain distribution distance. In addition, we build an adversarially
unified variational causal effect model, named VEGAN, with a novel two-stage
adversarial domain adaptation scheme to reduce the latent distribution
disparity between treated and control groups first, and between training and
runtime variables afterwards. We demonstrate that VEGAN outperforms other
state-of-the-art baselines on individual-level treatment effect estimation in
the presence of runtime domain corruption on benchmark datasets
A Study on Differentiable Logic and LLMs for EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition 2023
In this technical report, we present our findings from a study conducted on
the EPIC-KITCHENS-100 Unsupervised Domain Adaptation task for Action
Recognition. Our research focuses on the innovative application of a
differentiable logic loss in the training to leverage the co-occurrence
relations between verb and noun, as well as the pre-trained Large Language
Models (LLMs) to generate the logic rules for the adaptation to unseen action
labels. Specifically, the model's predictions are treated as the truth
assignment of a co-occurrence logic formula to compute the logic loss, which
measures the consistency between the predictions and the logic constraints. By
using the verb-noun co-occurrence matrix generated from the dataset, we observe
a moderate improvement in model performance compared to our baseline framework.
To further enhance the model's adaptability to novel action labels, we
experiment with rules generated using GPT-3.5, which leads to a slight decrease
in performance. These findings shed light on the potential and challenges of
incorporating differentiable logic and LLMs for knowledge extraction in
unsupervised domain adaptation for action recognition. Our final submission
(entitled `NS-LLM') achieved the first place in terms of top-1 action
recognition accuracy.Comment: Technical report submitted to CVPR 2023 EPIC-Kitchens challenge
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