title: YOLOv9: Learning What You Want to Learn Using Programmable Gradient
    Information
summary: 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.
paper_authors._id: 65d755fda92913cc5393a311
paper_authors.name: Chien-Yao Wang
paper_authors._id: 65d755fda92913cc5393a312
paper_authors.name: I-Hau Yeh
paper_authors._id: 65d755fda92913cc5393a313
paper_authors.name: Hong-Yuan Mark Liao
paper_title: YOLOv9: Learning What You Want to Learn Using Programmable Gradient
    Information
paper_summary: 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.
paper_ai_keywords: information bottleneck reversible functions programmable gradient information (PGI) gradient path planning Generalized Efficient Layer Aggregation Network (GELAN) conventional convolution operators depth-wise convolution parameter utilization train-from-scratch models pre-trained models