774 research outputs found
LIRA: Lifelong Image Restoration from Unknown Blended Distortions
Most existing image restoration networks are designed in a disposable way and
catastrophically forget previously learned distortions when trained on a new
distortion removal task. To alleviate this problem, we raise the novel lifelong
image restoration problem for blended distortions. We first design a base
fork-join model in which multiple pre-trained expert models specializing in
individual distortion removal task work cooperatively and adaptively to handle
blended distortions. When the input is degraded by a new distortion, inspired
by adult neurogenesis in human memory system, we develop a neural growing
strategy where the previously trained model can incorporate a new expert branch
and continually accumulate new knowledge without interfering with learned
knowledge. Experimental results show that the proposed approach can not only
achieve state-of-the-art performance on blended distortions removal tasks in
both PSNR/SSIM metrics, but also maintain old expertise while learning new
restoration tasks.Comment: ECCV2020 accepte
Spatial Mixture-of-Experts
Many data have an underlying dependence on spatial location; it may be
weather on the Earth, a simulation on a mesh, or a registered image. Yet this
feature is rarely taken advantage of, and violates common assumptions made by
many neural network layers, such as translation equivariance. Further, many
works that do incorporate locality fail to capture fine-grained structure. To
address this, we introduce the Spatial Mixture-of-Experts (SMoE) layer, a
sparsely-gated layer that learns spatial structure in the input domain and
routes experts at a fine-grained level to utilize it. We also develop new
techniques to train SMoEs, including a self-supervised routing loss and damping
expert errors. Finally, we show strong results for SMoEs on numerous tasks, and
set new state-of-the-art results for medium-range weather prediction and
post-processing ensemble weather forecasts.Comment: 20 pages, 3 figures; NeurIPS 202
Domain Generalization in Vision: A Survey
Generalization to out-of-distribution (OOD) data is a capability natural to
humans yet challenging for machines to reproduce. This is because most learning
algorithms strongly rely on the i.i.d.~assumption on source/target data, which
is often violated in practice due to domain shift. Domain generalization (DG)
aims to achieve OOD generalization by using only source data for model
learning. Since first introduced in 2011, research in DG has made great
progresses. In particular, intensive research in this topic has led to a broad
spectrum of methodologies, e.g., those based on domain alignment,
meta-learning, data augmentation, or ensemble learning, just to name a few; and
has covered various vision applications such as object recognition,
segmentation, action recognition, and person re-identification. In this paper,
for the first time a comprehensive literature review is provided to summarize
the developments in DG for computer vision over the past decade. Specifically,
we first cover the background by formally defining DG and relating it to other
research fields like domain adaptation and transfer learning. Second, we
conduct a thorough review into existing methods and present a categorization
based on their methodologies and motivations. Finally, we conclude this survey
with insights and discussions on future research directions.Comment: v4: includes the word "vision" in the title; improves the
organization and clarity in Section 2-3; adds future directions; and mor
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