355 research outputs found
Domain Conditioned Adaptation Network
Tremendous research efforts have been made to thrive deep domain adaptation
(DA) by seeking domain-invariant features. Most existing deep DA models only
focus on aligning feature representations of task-specific layers across
domains while integrating a totally shared convolutional architecture for
source and target. However, we argue that such strongly-shared convolutional
layers might be harmful for domain-specific feature learning when source and
target data distribution differs to a large extent. In this paper, we relax a
shared-convnets assumption made by previous DA methods and propose a Domain
Conditioned Adaptation Network (DCAN), which aims to excite distinct
convolutional channels with a domain conditioned channel attention mechanism.
As a result, the critical low-level domain-dependent knowledge could be
explored appropriately. As far as we know, this is the first work to explore
the domain-wise convolutional channel activation for deep DA networks.
Moreover, to effectively align high-level feature distributions across two
domains, we further deploy domain conditioned feature correction blocks after
task-specific layers, which will explicitly correct the domain discrepancy.
Extensive experiments on three cross-domain benchmarks demonstrate the proposed
approach outperforms existing methods by a large margin, especially on very
tough cross-domain learning tasks.Comment: Accepted by AAAI 202
Disentanglement by Cyclic Reconstruction
Deep neural networks have demonstrated their ability to automatically extract
meaningful features from data. However, in supervised learning, information
specific to the dataset used for training, but irrelevant to the task at hand,
may remain encoded in the extracted representations. This remaining information
introduces a domain-specific bias, weakening the generalization performance. In
this work, we propose splitting the information into a task-related
representation and its complementary context representation. We propose an
original method, combining adversarial feature predictors and cyclic
reconstruction, to disentangle these two representations in the single-domain
supervised case. We then adapt this method to the unsupervised domain
adaptation problem, consisting of training a model capable of performing on
both a source and a target domain. In particular, our method promotes
disentanglement in the target domain, despite the absence of training labels.
This enables the isolation of task-specific information from both domains and a
projection into a common representation. The task-specific representation
allows efficient transfer of knowledge acquired from the source domain to the
target domain. In the single-domain case, we demonstrate the quality of our
representations on information retrieval tasks and the generalization benefits
induced by sharpened task-specific representations. We then validate the
proposed method on several classical domain adaptation benchmarks and
illustrate the benefits of disentanglement for domain adaptation
PoliTO-IIT Submission to the EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition
In this report, we describe the technical details of our submission to the
EPIC-Kitchens-100 Unsupervised Domain Adaptation (UDA) Challenge in Action
Recognition. To tackle the domain-shift which exists under the UDA setting, we
first exploited a recent Domain Generalization (DG) technique, called Relative
Norm Alignment (RNA). It consists in designing a model able to generalize well
to any unseen domain, regardless of the possibility to access target data at
training time. Then, in a second phase, we extended the approach to work on
unlabelled target data, allowing the model to adapt to the target distribution
in an unsupervised fashion. For this purpose, we included in our framework
existing UDA algorithms, such as Temporal Attentive Adversarial Adaptation
Network (TA3N), jointly with new multi-stream consistency losses, namely
Temporal Hard Norm Alignment (T-HNA) and Min-Entropy Consistency (MEC). Our
submission (entry 'plnet') is visible on the leaderboard and it achieved the
1st position for 'verb', and the 3rd position for both 'noun' and 'action'.Comment: 3rd place in the 2021 EPIC-KITCHENS-100 Unsupervised Domain
Adaptation Challenge for Action Recognitio
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