During the past decade, deep neural networks have led to fast-paced progress
and significant achievements in computer vision problems, for both academia and
industry. Yet despite their success, state-of-the-art image classification
approaches fail to generalize well in previously unseen visual contexts, as
required by many real-world applications. In this paper, we focus on this
domain generalization (DG) problem and argue that the generalization ability of
deep convolutional neural networks can be improved by taking advantage of
multi-layer and multi-scaled representations of the network. We introduce a
framework that aims at improving domain generalization of image classifiers by
combining both low-level and high-level features at multiple scales, enabling
the network to implicitly disentangle representations in its latent space and
learn domain-invariant attributes of the depicted objects. Additionally, to
further facilitate robust representation learning, we propose a novel objective
function, inspired by contrastive learning, which aims at constraining the
extracted representations to remain invariant under distribution shifts. We
demonstrate the effectiveness of our method by evaluating on the domain
generalization datasets of PACS, VLCS, Office-Home and NICO. Through extensive
experimentation, we show that our model is able to surpass the performance of
previous DG methods and consistently produce competitive and state-of-the-art
results in all datasets.

通过结合多层和多尺度的特征，利用深度卷积神经网络来提高图像分类器的领域泛化能力，并提出了一种新颖的目标函数，使用对比学习的方法约束提取的特征在分布转移下保持不变，实验证明该方法在多个数据集上均表现出与之前方法相比更好的性能。

多尺度和多层次对比学习用于领域泛化

Multi-Scale and Multi-Layer Contrastive Learning for Domain  Generalization

We consider the problem of collectively detecting multiple events,
particularly in cross-sentence settings. The key to dealing with the problem is
to encode semantic information and model event inter-dependency at a
document-level. In this paper, we reformulate it as a Seq2Seq task and propose
a Multi-Layer Bidirectional Network (MLBiNet) to capture the document-level
association of events and semantic information simultaneously. Specifically, a
bidirectional decoder is firstly devised to model event inter-dependency within
a sentence when decoding the event tag vector sequence. Secondly, an
information aggregation module is employed to aggregate sentence-level semantic
and event tag information. Finally, we stack multiple bidirectional decoders
and feed cross-sentence information, forming a multi-layer bidirectional
tagging architecture to iteratively propagate information across sentences. We
show that our approach provides significant improvement in performance compared
to the current state-of-the-art results.

本文提出一种基于 Seq2Seq 的多层双向网络（MLBiNet）模型，用于在跨句子的情况下集体检测多个事件，并利用信息聚合模块和多层双向编码器来编码语义信息和事件之间的关联，从而提高跨句子检测的性能。

MLBiNet：跨句子事件集合检测网络

MLBiNet: A Cross-Sentence Collective Event Detection Network

In this paper, we introduce a new channel pruning method to accelerate very
deep convolutional neural networks.Given a trained CNN model, we propose an
iterative two-step algorithm to effectively prune each layer, by a LASSO
regression based channel selection and least square reconstruction. We further
generalize this algorithm to multi-layer and multi-branch cases. Our method
reduces the accumulated error and enhance the compatibility with various
architectures. Our pruned VGG-16 achieves the state-of-the-art results by 5x
speed-up along with only 0.3% increase of error. More importantly, our method
is able to accelerate modern networks like ResNet, Xception and suffers only
1.4%, 1.0% accuracy loss under 2x speed-up respectively, which is significant.
Code has been made publicly available.

本文提出了一种新的通道剪枝方法，能够加速非常深的卷积神经网络，通过基于 LASSO 回归的通道选择和最小二乘重构的迭代两步算法有效地修剪每一层，进一步推广到多层和多分支情况，剪枝后的 VGG-16 实现了 5 倍速度提升和仅 0.3％误差增加的最新成果，更重要的是，本方法能够加速现代网络，例如 ResNet，Xception，分别在 2 倍加速下只有 1.4％，1.0％的准确度损失，代码已公开出售。