27 research outputs found
Regularized Evolutionary Algorithm for Dynamic Neural Topology Search
Designing neural networks for object recognition requires considerable
architecture engineering. As a remedy, neuro-evolutionary network architecture
search, which automatically searches for optimal network architectures using
evolutionary algorithms, has recently become very popular. Although very
effective, evolutionary algorithms rely heavily on having a large population of
individuals (i.e., network architectures) and is therefore memory expensive. In
this work, we propose a Regularized Evolutionary Algorithm with low memory
footprint to evolve a dynamic image classifier. In details, we introduce novel
custom operators that regularize the evolutionary process of a micro-population
of 10 individuals. We conduct experiments on three different digits datasets
(MNIST, USPS, SVHN) and show that our evolutionary method obtains competitive
results with the current state-of-the-art
Learning Across Decentralized Multi-Modal Remote Sensing Archives with Federated Learning
The development of federated learning (FL) methods, which aim to learn from
distributed databases (i.e., clients) without accessing data on clients, has
recently attracted great attention. Most of these methods assume that the
clients are associated with the same data modality. However, remote sensing
(RS) images in different clients can be associated with different data
modalities that can improve the classification performance when jointly used.
To address this problem, in this paper we introduce a novel multi-modal FL
framework that aims to learn from decentralized multi-modal RS image archives
for RS image classification problems. The proposed framework is made up of
three modules: 1) multi-modal fusion (MF); 2) feature whitening (FW); and 3)
mutual information maximization (MIM). The MF module performs iterative model
averaging to learn without accessing data on clients in the case that clients
are associated with different data modalities. The FW module aligns the
representations learned among the different clients. The MIM module maximizes
the similarity of images from different modalities. Experimental results show
the effectiveness of the proposed framework compared to iterative model
averaging, which is a widely used algorithm in FL. The code of the proposed
framework is publicly available at https://git.tu-berlin.de/rsim/MM-FL.Comment: Accepted at IEEE International Geoscience and Remote Sensing
Symposium (IGARSS) 2023. Our code is available at
https://git.tu-berlin.de/rsim/MM-F
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
Manifold-Aware Self-Training for Unsupervised Domain Adaptation on Regressing 6D Object Pose
Domain gap between synthetic and real data in visual regression (e.g. 6D pose
estimation) is bridged in this paper via global feature alignment and local
refinement on the coarse classification of discretized anchor classes in target
space, which imposes a piece-wise target manifold regularization into
domain-invariant representation learning. Specifically, our method incorporates
an explicit self-supervised manifold regularization, revealing consistent
cumulative target dependency across domains, to a self-training scheme (e.g.
the popular Self-Paced Self-Training) to encourage more discriminative
transferable representations of regression tasks. Moreover, learning unified
implicit neural functions to estimate relative direction and distance of
targets to their nearest class bins aims to refine target classification
predictions, which can gain robust performance against inconsistent feature
scaling sensitive to UDA regressors. Experiment results on three public
benchmarks of the challenging 6D pose estimation task can verify the
effectiveness of our method, consistently achieving superior performance to the
state-of-the-art for UDA on 6D pose estimation.Comment: Accepted by IJCAI 202
Bi-Directional Generation for Unsupervised Domain Adaptation
Unsupervised domain adaptation facilitates the unlabeled target domain
relying on well-established source domain information. The conventional methods
forcefully reducing the domain discrepancy in the latent space will result in
the destruction of intrinsic data structure. To balance the mitigation of
domain gap and the preservation of the inherent structure, we propose a
Bi-Directional Generation domain adaptation model with consistent classifiers
interpolating two intermediate domains to bridge source and target domains.
Specifically, two cross-domain generators are employed to synthesize one domain
conditioned on the other. The performance of our proposed method can be further
enhanced by the consistent classifiers and the cross-domain alignment
constraints. We also design two classifiers which are jointly optimized to
maximize the consistency on target sample prediction. Extensive experiments
verify that our proposed model outperforms the state-of-the-art on standard
cross domain visual benchmarks.Comment: 9 pages, 4 figure