Recurrent neural networks and Transformers have recently dominated most
applications in hyperspectral (HS) imaging, owing to their capability to
capture long-range dependencies from spectrum sequences. However, despite the
success of these sequential architectures, the non-ignorable inefficiency
caused by either difficulty in parallelization or computationally prohibitive
attention still hinders their practicality, especially for large-scale
observation in remote sensing scenarios. To address this issue, we herein
propose SpectralMamba -- a novel state space model incorporated efficient deep
learning framework for HS image classification. SpectralMamba features the
simplified but adequate modeling of HS data dynamics at two levels. First, in
spatial-spectral space, a dynamical mask is learned by efficient convolutions
to simultaneously encode spatial regularity and spectral peculiarity, thus
attenuating the spectral variability and confusion in discriminative
representation learning. Second, the merged spectrum can then be efficiently
operated in the hidden state space with all parameters learned input-dependent,
yielding selectively focused responses without reliance on redundant attention
or imparallelizable recurrence. To explore the room for further computational
downsizing, a piece-wise scanning mechanism is employed in-between,
transferring approximately continuous spectrum into sequences with squeezed
length while maintaining short- and long-term contextual profiles among
hundreds of bands. Through extensive experiments on four benchmark HS datasets
acquired by satellite-, aircraft-, and UAV-borne imagers, SpectralMamba
surprisingly creates promising win-wins from both performance and efficiency
perspectives.

SpectralMamba 是一种高效的深度学习框架，结合了状态空间模型来进行高光谱图像分类，通过学习动态掩膜和合并光谱的操作在隐藏状态空间中实现了选择性的响应，同时在计算效率和性能角度上取得了令人满意的成果。

SpectralMamba：高效的高光谱图像分类 Mamba

SpectralMamba: Efficient Mamba for Hyperspectral Image Classification

In the field of deep learning, the prevalence of models initially trained
with 32-bit precision is a testament to its robustness and accuracy. However,
the continuous evolution of these models often demands further training, which
can be resource-intensive. This study introduces a novel approach where we
continue the training of these pre-existing 32-bit models using 16-bit
precision. This technique not only caters to the need for efficiency in
computational resources but also significantly improves the speed of additional
training phases. By adopting 16-bit precision for ongoing training, we are able
to substantially decrease memory requirements and computational burden, thereby
accelerating the training process in a resource-limited setting. Our
experiments show that this method maintains the high standards of accuracy set
by the original 32-bit training while providing a much-needed boost in training
speed. This approach is especially pertinent in today's context, where most
models are initially trained in 32-bit and require periodic updates and
refinements. The findings from our research suggest that this strategy of
16-bit continuation training can be a key solution for sustainable and
efficient deep learning, offering a practical way to enhance pre-trained models
rapidly and in a resource-conscious manner.

本研究介绍了一种新方法，通过使用 16 位精度持续训练已存在的 32 位精度的模型，从而在保证准确性的同时显著提高训练速度和资源利用效率。该方法为有限资源环境中的深度学习提供了可行的加速和优化方案。

连续 16 位训练：加速 32 位预训练神经网络

Continuous 16-bit Training: Accelerating 32-bit Pre-Trained Neural  Networks

We propose Falcon, an end-to-end 3-party protocol for efficient private
training and inference of large machine learning models. Falcon presents four
main advantages - (i) It is highly expressive with support for high capacity
networks such as VGG16 (ii) it supports batch normalization which is important
for training complex networks such as AlexNet (iii) Falcon guarantees security
with abort against malicious adversaries, assuming an honest majority (iv)
Lastly, Falcon presents new theoretical insights for protocol design that make
it highly efficient and allow it to outperform existing secure deep learning
solutions. Compared to prior art for private inference, we are about 8x faster
than SecureNN (PETS'19) on average and comparable to ABY3 (CCS'18). We are
about 16-200x more communication efficient than either of these. For private
training, we are about 6x faster than SecureNN, 4.4x faster than ABY3 and about
2-60x more communication efficient. Our experiments in the WAN setting show
that over large networks and datasets, compute operations dominate the overall
latency of MPC, as opposed to the communication.

本文提出了一种名为 Falcon 的端到端的 3 方协议，用于高效的私有机器学习模型训练和推理，它支持高效网络并且包容批量归一化，有安全性保证，并且比先前的解决方案更快速和更高效。