Today's deep learning methods focus on how to design the most appropriate objective functions so that the prediction results of the model can be closest to the ground truth. Meanwhile, an appropriate architecture that can facilitate acquisition of enough information for prediction has to be designed. Existing methods ignore a fact that when input data undergoes layer-by-layer feature extraction and spatial transformation, large amount of information will be lost. This paper will delve into the important issues of data loss when data is transmitted through deep networks, namely information bottleneck and reversible functions. We proposed the concept of programmable gradient information (PGI) to cope with the various changes required by deep networks to achieve multiple objectives. PGI can provide complete input information for the target task to calculate objective function, so that reliable gradient information can be obtained to update network weights. In addition, a new lightweight network architecture -- Generalized Efficient Layer Aggregation Network (GELAN), based on gradient path planning is designed. GELAN's architecture confirms that PGI has gained superior results on lightweight models. We verified the proposed GELAN and PGI on MS COCO dataset based object detection. The results show that GELAN only uses conventional convolution operators to achieve better parameter utilization than the state-of-the-art methods developed based on depth-wise convolution. PGI can be used for variety of models from lightweight to large. It can be used to obtain complete information, so that train-from-scratch models can achieve better results than state-of-the-art models pre-trained using large datasets, the comparison results are shown in Figure 1. The source codes are at: https://github.com/WongKinYiu/yolov9.

翻译：当前深度学习方法主要聚焦于设计最优目标函数，以使模型预测结果尽可能接近真实值。同时，需要设计能够促进获取足够预测信息的网络架构。现有方法忽略了关键事实：输入数据经过逐层特征提取和空间变换时会产生大量信息损失。本文深入探讨深度网络中数据传输过程中的关键问题——信息瓶颈与可逆函数。我们提出可编程梯度信息（PGI）概念，以应对深度网络为实现多目标所需的各种变化。PGI能够为目标任务提供完整的输入信息用于计算目标函数，从而获取可靠梯度信息以更新网络权重。此外，基于梯度路径规划设计了新型轻量级网络架构——广义高效层聚合网络（GELAN）。GELAN架构验证了PGI在轻量级模型上的优越表现。我们在MS COCO数据集上进行了基于目标检测的验证实验。结果表明，GELAN仅使用常规卷积算子即能实现优于基于深度可分离卷积的最优方法的参数利用率。PGI可适用于从轻量级到大型的多种模型。通过获取完整信息，从零训练的模型能够达到优于使用大型数据集预训练的最优模型的效果，对比结果如图1所示。源代码见：https://github.com/WongKinYiu/yolov9。