4,333 research outputs found
Phase-locked sustainment of photorefractive holograms using phase conjugation
A method for sustaining multiply exposed photorefractive holograms, in a phase-locked fashion, by using a pair of phase-conjugating mirrors is described. It is shown that a steady state exists where the overall diffraction efficiency is independent of the number of holographic exposures and the final holograms are exactly in phase with the initial ones. Both analytical and experimental results are presented
ACO-based Multi-Objective Scheduling of Identical Parallel Batch Processing Machines in Semiconductor Manufacturing
Preserving Linear Separability in Continual Learning by Backward Feature Projection
Catastrophic forgetting has been a major challenge in continual learning,
where the model needs to learn new tasks with limited or no access to data from
previously seen tasks. To tackle this challenge, methods based on knowledge
distillation in feature space have been proposed and shown to reduce
forgetting. However, most feature distillation methods directly constrain the
new features to match the old ones, overlooking the need for plasticity. To
achieve a better stability-plasticity trade-off, we propose Backward Feature
Projection (BFP), a method for continual learning that allows the new features
to change up to a learnable linear transformation of the old features. BFP
preserves the linear separability of the old classes while allowing the
emergence of new feature directions to accommodate new classes. BFP can be
integrated with existing experience replay methods and boost performance by a
significant margin. We also demonstrate that BFP helps learn a better
representation space, in which linear separability is well preserved during
continual learning and linear probing achieves high classification accuracy.
The code can be found at https://github.com/rvl-lab-utoronto/BFPComment: CVPR 2023. The code can be found at
https://github.com/rvl-lab-utoronto/BF
Structural Origins of the Enhancement in Ionic Conductivity of a Chalcogenide Compound by Adding AgI
Networks are Slacking Off: Understanding Generalization Problem in Image Deraining
Deep deraining networks, while successful in laboratory benchmarks,
consistently encounter substantial generalization issues when deployed in
real-world applications. A prevailing perspective in deep learning encourages
the use of highly complex training data, with the expectation that a richer
image content knowledge will facilitate overcoming the generalization problem.
However, through comprehensive and systematic experimentation, we discovered
that this strategy does not enhance the generalization capability of these
networks. On the contrary, it exacerbates the tendency of networks to overfit
to specific degradations. Our experiments reveal that better generalization in
a deraining network can be achieved by simplifying the complexity of the
training data. This is due to the networks are slacking off during training,
that is, learning the least complex elements in the image content and
degradation to minimize training loss. When the complexity of the background
image is less than that of the rain streaks, the network will prioritize the
reconstruction of the background, thereby avoiding overfitting to the rain
patterns and resulting in improved generalization performance. Our research not
only offers a valuable perspective and methodology for better understanding the
generalization problem in low-level vision tasks, but also displays promising
practical potential
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