Visualizing CNN is an important part in building trust and explaining model's
prediction. Methods like CAM and GradCAM have been really successful in
localizing area of the image responsible for the output but are only limited to
classification models. In this paper, we present a new method EmbeddingCAM,
which generalizes the Grad-CAM for embedding networks. We show that for
classification networks, EmbeddingCAM reduces to GradCAM. We show the
effectiveness of our method on CUB-200-2011 dataset and also present
quantitative and qualitative analysis on the dataset.

本研究提出一种名为 EmbeddingCAM 的新方法，扩展了 Grad-CAM 以适用于嵌入网络，通过在 CUB-200-2011 数据集上的实证和定量分析，证明了该方法的有效性。

泛化 Embedding 网络的 GradCAM

Generalizing GradCAM for Embedding Networks

Incorporating symmetries can lead to highly data-efficient and generalizable
models by defining equivalence classes of data samples related by
transformations. However, characterizing how transformations act on input data
is often difficult, limiting the applicability of equivariant models. We
propose learning symmetric embedding networks (SENs) that encode an input space
(e.g. images), where we do not know the effect of transformations (e.g.
rotations), to a feature space that transforms in a known manner under these
operations. This network can be trained end-to-end with an equivariant task
network to learn an explicitly symmetric representation. We validate this
approach in the context of equivariant transition models with 3 distinct forms
of symmetry. Our experiments demonstrate that SENs facilitate the application
of equivariant networks to data with complex symmetry representations.
Moreover, doing so can yield improvements in accuracy and generalization
relative to both fully-equivariant and non-equivariant baselines.

利用学习对称嵌入网络（SENs）的方法能够促进等变网络应用于具有复杂对称性表示的数据，并可能相对于全等变和非等变基线产生精度和泛化方面的改进。

学习对称嵌入以实现等变世界模型

Learning Symmetric Embeddings for Equivariant World Models

Class-incremental learning of deep networks sequentially increases the number
of classes to be classified. During training, the network has only access to
data of one task at a time, where each task contains several classes. In this
setting, networks suffer from catastrophic forgetting which refers to the
drastic drop in performance on previous tasks. The vast majority of methods
have studied this scenario for classification networks, where for each new task
the classification layer of the network must be augmented with additional
weights to make room for the newly added classes. Embedding networks have the
advantage that new classes can be naturally included into the network without
adding new weights. Therefore, we study incremental learning for embedding
networks. In addition, we propose a new method to estimate the drift, called
semantic drift, of features and compensate for it without the need of any
exemplars. We approximate the drift of previous tasks based on the drift that
is experienced by current task data. We perform experiments on fine-grained
datasets, CIFAR100 and ImageNet-Subset. We demonstrate that embedding networks
suffer significantly less from catastrophic forgetting. We outperform existing
methods which do not require exemplars and obtain competitive results compared
to methods which store exemplars. Furthermore, we show that our proposed SDC
when combined with existing methods to prevent forgetting consistently improves
results.

该研究针对增量学习设计了一种新方法，使用嵌入网络，以自然方式加入新类别而无需添加新的权重，并且通过估计特征的语义漂移来避免严重遗忘。实验结果表明，该方法表现优异且能显著减少遗忘。