We explore the field of wildlife re-identification by implementing an MLP
from scratch using NumPy, A DCNN using Keras, and a binary classifier with
LightGBM for the purpose of learning for an assignment. Analyzing the
performance of multiple models on multiple datasets. We attempt to replicate
prior research in metric learning for wildlife re-identification. Firstly, we
find that the usage of MLPs trained for classification, then removing the
output layer and using the second last layer as an embedding was not a
successful strategy for similar learning; it seems like losses designed for
embeddings such as triplet loss are required. The DCNNS performed well on some
datasets but poorly on others, which did not align with findings in previous
literature. The LightGBM classifier overfitted too heavily and was not
significantly better than a constant model when trained and evaluated on all
pairs using accuracy as a metric. The technical implementations used seem to
match standards according to comparisons with documentation examples and good
results on certain datasets. However, there is still more to explore in regards
to being able to fully recreate past literature.

我们通过使用 NumPy 从头实现 MLP，使用 Keras 进行 DCNN，以及使用 LightGBM 进行二元分类器，探索了野生动物重新识别领域，并分析了多模型在多数据集上的性能表现。

通過野生動物再識別增強理解能力

Enhancing Understanding Through Wildlife Re-Identification

Normalization layers are essential in a Deep Convolutional Neural Network
(DCNN). Various normalization methods have been proposed. The statistics used
to normalize the feature maps can be computed at batch, channel, or instance
level. However, in most of existing methods, the normalization for each layer
is fixed. Batch-Instance Normalization (BIN) is one of the first proposed
methods that combines two different normalization methods and achieve diverse
normalization for different layers. However, two potential issues exist in BIN:
first, the Clip function is not differentiable at input values of 0 and 1;
second, the combined feature map is not with a normalized distribution which is
harmful for signal propagation in DCNN. In this paper, an Instance-Layer
Normalization (ILN) layer is proposed by using the Sigmoid function for the
feature map combination, and cascading group normalization. The performance of
ILN is validated on image segmentation of the Right Ventricle (RV) and Left
Ventricle (LV) using U-Net as the network architecture. The results show that
the proposed ILN outperforms previous traditional and popular normalization
methods with noticeable accuracy improvements for most validations, supporting
the effectiveness of the proposed ILN.

本文提出了一种基于 Sigmoid 函数和分组归一化的 Instance-Layer Normalization (ILN) 层，用于 DCNN 的浅层特征图融合。使用 U-Net 作为 DCNN 架构以右心室和左心室的图像分割为例，验证了 ILN 在各种常见规格网络上的有效性，优于传统的与流行的归一化方法。

使用实例层规范化的 U-Net 训练

U-Net Training with Instance-Layer Normalization

Recently, there has been a flurry of industrial activity around logo
recognition, such as Ditto's service for marketers to track their brands in
user-generated images, and LogoGrab's mobile app platform for logo recognition.
However, relatively little academic or open-source logo recognition progress
has been made in the last four years. Meanwhile, deep convolutional neural
networks (DCNNs) have revolutionized a broad range of object recognition
applications. In this work, we apply DCNNs to logo recognition. We propose
several DCNN architectures, with which we surpass published state-of-art
accuracy on a popular logo recognition dataset.

本文提出基于深度卷积神经网络的标志识别方法，并在一项流行的标志识别数据集上的准确性方面超过已发表的最新技术水平。