Awesome GitHub RepositoriesShared-Bottom Architectures

DeepCTR is a specialized software framework and deep learning model library designed for predicting click-through rates and implementing recommendation systems. It provides a suite of tabular data models and architectures tailored for binary classification and sparse feature processing. The framework includes dedicated toolkits for multi-task learning and sequential interest modeling. It allows for the simultaneous estimation of multiple related targets through shared-bottom and gated expert neural networks, while capturing evolving user behavior using attention mechanisms and transformers.

Sapiens 是一个高分辨率人体视觉模型，专为高精度、以人为中心的计算机视觉任务而设计。它是一套用于估计人体姿态、深度和表面几何形状的工具集。 该项目利用视觉 Transformer 主干网络通过共享编码器执行多项任务。这种架构能够同时预测骨骼结构、关节位置以及相机与人体对象之间的距离。 该模型的功能涵盖了人体部位分割（从背景中分离解剖区域）和表面法线预测（从 2D 图像中恢复 3D 几何细节）。这些任务由一个采用像素级回归和语义分割掩码的多任务学习框架提供支持。

DeepCTR-Torch is a deep learning library for building click-through rate prediction models. It provides a modular framework for assembling custom prediction architectures from pre-built core, interaction, and sequence layers, enabling the construction of deep neural networks that estimate click probability from user behavior data. The library specializes in feature interaction modeling, offering components for learning low-order, high-order, and adaptive-order feature crosses. It supports multi-task learning for predicting multiple objectives simultaneously, such as click and conversion rates