techniques. To recap, learning techniques can help us meet our model quality goals. Techniques like distillation and data augmentation improve the model quality, without increasing the footprint of the model training compute budget, so this approach is a non-starter. While techniques like data-augmentation, distillation etc. as introduced in chapter 3 do help us achieve better quality with fewer labels and fewer techniques like distillation might not be as helpful in certain settings. Subclass distillation in the next subsection can help us in some of these cases. Let’s find out how. Subclass Distillation It can also

briefly introduced learning techniques such as regularization, dropout, data augmentation, and distillation to improve quality. These techniques can boost metrics like accuracy, precision, recall, etc. often are our primary quality concerns. We have chosen two of them, namely data augmentation and distillation, to discuss in this chapter. This is because, firstly, regularization and dropout are fairly straight-forward straight-forward to enable in any modern deep learning framework. Secondly, data augmentation and distillation can bring significant efficiency gains during the training phase, which is the focus of this chapter

efficient model by trimming the number of parameters if needed. An example of a learning technique is Distillation (see Figure 1-10), which helps a smaller model (student) that can be deployed, to learn from a of probabilities for each of the possible classes according to the teacher model. Figure 1-10: Distillation of a smaller student model from a larger pre-trained teacher model. Both the teacher’s weights way. In the original paper which proposed distillation, Hinton et al. replicated performance of an ensemble of 10 models with one model when using distillation. For vision datasets like CIFAR-10, an accuracy

model using data augmentation to achieve higher performance and subsequently apply compression or distillation to further reduce its footprint. With this chapter, we hope to have set the stage for your exploration your deep learning projects. They can often be combined with other approaches like quantization, distillation, data augmentation, that we already learned. In the next chapter we will explore some more advanced

裁剪和知识蒸馏方式效果对比 在美团搜索核心排序的业务场景下，我们采用知识蒸馏使得 BERT 模型在对响应时 间要求苛刻的搜索场景下符合了上线的要求，并且效果无显著的性能损失。知识蒸 馏（Knowledge Distillation）核心思想是通过迁移知识，从而通过训练好的大模型 得到更加适合推理的小模型。首先我们基于 MT-BERT（12 Layers），在大规模的 美团点评业务语料上进行知识蒸馏得到通用的 MT-BERT Knowledge in a Neural Network. 2015. [7] Yew Ken Chia et al.Transformer to CNN: Label-scarce distillation for efficient text classification. 2018. [8] K-BERT: Enabling Language Representation with

2 YOLOv6 量化感知蒸馏框架 针 对 YOLOv6s， 我 们 选 择 对 Neck（Rep-PAN）输 出 的 特 征 图 进 行 通 道 蒸 馏 （channel-wise distillation, CW)。另外，我们采用“自蒸馏”的方法，教师模型是 FP32 精度的 YOLOv6s，学生模型是 INT8 精度的 YOLOv6s。下图 7 是一个简化 示意图，只画出了 Neck Nsight-systems: https://docs.nvidia.com/nsight-systems/UserGuide/index.html [6] Channel-wise Knowledge Distillation for Dense Prediction, https://arxiv.org/ abs/2011.13256 [7] YOLOv6: A Single-Stage Object Detection https://tech.meituan.com/2021/07/08/multi-business-modeling.html. [7] Tang, Jiaxi, and Ke Wang. “Ranking distillation: Learning compact ranking models with high performance for recommender system.” Proceedings

application-independent set of baked geometric results. This 'distillation' of scenes into baked geometry is exactly analogous to the distillation of lighting and rendering scenes into rendered image data

application-independent set of baked geometric results. This ‘distillation’ of scenes into baked geometry is exactly analogous to the distillation of lighting and rendering scenes into rendered image data

application-independent set of baked geometric results. This 'distillation' of scenes into baked geometry is exactly analogous to the distillation of lighting and rendering scenes into rendered image data

application-independent set of baked geometric results. This ‘distillation’ of scenes into baked geometry is exactly analogous to the distillation of lighting and rendering scenes into rendered image data