18,554 research outputs found
Unsupervised Domain Adaptation by Backpropagation
Top-performing deep architectures are trained on massive amounts of labeled
data. In the absence of labeled data for a certain task, domain adaptation
often provides an attractive option given that labeled data of similar nature
but from a different domain (e.g. synthetic images) are available. Here, we
propose a new approach to domain adaptation in deep architectures that can be
trained on large amount of labeled data from the source domain and large amount
of unlabeled data from the target domain (no labeled target-domain data is
necessary).
As the training progresses, the approach promotes the emergence of "deep"
features that are (i) discriminative for the main learning task on the source
domain and (ii) invariant with respect to the shift between the domains. We
show that this adaptation behaviour can be achieved in almost any feed-forward
model by augmenting it with few standard layers and a simple new gradient
reversal layer. The resulting augmented architecture can be trained using
standard backpropagation.
Overall, the approach can be implemented with little effort using any of the
deep-learning packages. The method performs very well in a series of image
classification experiments, achieving adaptation effect in the presence of big
domain shifts and outperforming previous state-of-the-art on Office datasets
Transfer Learning via Contextual Invariants for One-to-Many Cross-Domain Recommendation
The rapid proliferation of new users and items on the social web has
aggravated the gray-sheep user/long-tail item challenge in recommender systems.
Historically, cross-domain co-clustering methods have successfully leveraged
shared users and items across dense and sparse domains to improve inference
quality. However, they rely on shared rating data and cannot scale to multiple
sparse target domains (i.e., the one-to-many transfer setting). This, combined
with the increasing adoption of neural recommender architectures, motivates us
to develop scalable neural layer-transfer approaches for cross-domain learning.
Our key intuition is to guide neural collaborative filtering with
domain-invariant components shared across the dense and sparse domains,
improving the user and item representations learned in the sparse domains. We
leverage contextual invariances across domains to develop these shared modules,
and demonstrate that with user-item interaction context, we can learn-to-learn
informative representation spaces even with sparse interaction data. We show
the effectiveness and scalability of our approach on two public datasets and a
massive transaction dataset from Visa, a global payments technology company
(19% Item Recall, 3x faster vs. training separate models for each domain). Our
approach is applicable to both implicit and explicit feedback settings.Comment: SIGIR 202
AutoDIAL: Automatic DomaIn Alignment Layers
Classifiers trained on given databases perform poorly when tested on data
acquired in different settings. This is explained in domain adaptation through
a shift among distributions of the source and target domains. Attempts to align
them have traditionally resulted in works reducing the domain shift by
introducing appropriate loss terms, measuring the discrepancies between source
and target distributions, in the objective function. Here we take a different
route, proposing to align the learned representations by embedding in any given
network specific Domain Alignment Layers, designed to match the source and
target feature distributions to a reference one. Opposite to previous works
which define a priori in which layers adaptation should be performed, our
method is able to automatically learn the degree of feature alignment required
at different levels of the deep network. Thorough experiments on different
public benchmarks, in the unsupervised setting, confirm the power of our
approach.Comment: arXiv admin note: substantial text overlap with arXiv:1702.06332
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