8 research outputs found
Wavelet Integrated CNNs for Noise-Robust Image Classification
Convolutional Neural Networks (CNNs) are generally prone to noise
interruptions, i.e., small image noise can cause drastic changes in the output.
To suppress the noise effect to the final predication, we enhance CNNs by
replacing max-pooling, strided-convolution, and average-pooling with Discrete
Wavelet Transform (DWT). We present general DWT and Inverse DWT (IDWT) layers
applicable to various wavelets like Haar, Daubechies, and Cohen, etc., and
design wavelet integrated CNNs (WaveCNets) using these layers for image
classification. In WaveCNets, feature maps are decomposed into the
low-frequency and high-frequency components during the down-sampling. The
low-frequency component stores main information including the basic object
structures, which is transmitted into the subsequent layers to extract robust
high-level features. The high-frequency components, containing most of the data
noise, are dropped during inference to improve the noise-robustness of the
WaveCNets. Our experimental results on ImageNet and ImageNet-C (the noisy
version of ImageNet) show that WaveCNets, the wavelet integrated versions of
VGG, ResNets, and DenseNet, achieve higher accuracy and better noise-robustness
than their vanilla versions.Comment: CVPR accepted pape
Cross-resolution Face Recognition via Identity-Preserving Network and Knowledge Distillation
Cross-resolution face recognition has become a challenging problem for modern
deep face recognition systems. It aims at matching a low-resolution probe image
with high-resolution gallery images registered in a database. Existing methods
mainly leverage prior information from high-resolution images by either
reconstructing facial details with super-resolution techniques or learning a
unified feature space. To address this challenge, this paper proposes a new
approach that enforces the network to focus on the discriminative information
stored in the low-frequency components of a low-resolution image. A
cross-resolution knowledge distillation paradigm is first employed as the
learning framework. Then, an identity-preserving network, WaveResNet, and a
wavelet similarity loss are designed to capture low-frequency details and boost
performance. Finally, an image degradation model is conceived to simulate more
realistic low-resolution training data. Consequently, extensive experimental
results show that the proposed method consistently outperforms the baseline
model and other state-of-the-art methods across a variety of image resolutions
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A Lattice-Structure-Based Trainable Orthogonal Wavelet Unit for Image Classification
Triple-View Knowledge Distillation for Semi-Supervised Semantic Segmentation
To alleviate the expensive human labeling, semi-supervised semantic
segmentation employs a few labeled images and an abundant of unlabeled images
to predict the pixel-level label map with the same size. Previous methods often
adopt co-training using two convolutional networks with the same architecture
but different initialization, which fails to capture the sufficiently diverse
features. This motivates us to use tri-training and develop the triple-view
encoder to utilize the encoders with different architectures to derive diverse
features, and exploit the knowledge distillation skill to learn the
complementary semantics among these encoders. Moreover, existing methods simply
concatenate the features from both encoder and decoder, resulting in redundant
features that require large memory cost. This inspires us to devise a
dual-frequency decoder that selects those important features by projecting the
features from the spatial domain to the frequency domain, where the
dual-frequency channel attention mechanism is introduced to model the feature
importance. Therefore, we propose a Triple-view Knowledge Distillation
framework, termed TriKD, for semi-supervised semantic segmentation, including
the triple-view encoder and the dual-frequency decoder. Extensive experiments
were conducted on two benchmarks, \ie, Pascal VOC 2012 and Cityscapes, whose
results verify the superiority of the proposed method with a good tradeoff
between precision and inference speed
Photonic Technology for Precision Metrology
Photonics has had a decisive influence on recent scientific and technological achievements. It includes aspects of photon generation and photon–matter interaction. Although it finds many applications in the whole optical range of the wavelengths, most solutions operate in the visible and infrared range. Since the invention of the laser, a source of highly coherent optical radiation, optical measurements have become the perfect tool for highly precise and accurate measurements. Such measurements have the additional advantages of requiring no contact and a fast rate suitable for in-process metrology. However, their extreme precision is ultimately limited by, e.g., the noise of both lasers and photodetectors. The Special Issue of the Applied Science is devoted to the cutting-edge uses of optical sources, detectors, and optoelectronics systems in numerous fields of science and technology (e.g., industry, environment, healthcare, telecommunication, security, and space). The aim is to provide detail on state-of-the-art photonic technology for precision metrology and identify future developmental directions. This issue focuses on metrology principles and measurement instrumentation in optical technology to solve challenging engineering problems