WebJan 19, 2024 · We propose a form of gradient surgery that projects a task's gradient onto the normal plane of the gradient of any other task that has a conflicting gradient. On a series of challenging multi-task supervised and multi-task RL problems, this approach leads to substantial gains in efficiency and performance. WebPytorch reimplementation for "Gradient Surgery for Multi-Task Learning" Topics reinforcement-learning deep-learning deep-reinforcement-learning pytorch mnist rl reimplementation multi-task-learning cifar-100 multi-task …

Review for NeurIPS paper: Gradient Surgery for Multi-Task Learning

Webdevise novel gradient agreement strategies based on gradi-ent surgery to alleviate their effect. The gradient surgery framework was introduced in [36] to address multi-task learning, and is rooted in a simple and intuitive idea. In general, deep neural networks are trained using gradient descent, where gradients guide the optimiza- WebGradient Surgery for Multi-Task Learning Figure 2: Conflicting gradients and PCGrad. In (a), tasks iand j have conflicting gradient directions, which can lead to destructive … dhs single women shelter

Gradient Surgery for Multi-Task Learning Request PDF

WebNIPS WebIn this work, we identify a set of three conditions of the multi-task optimization landscape that cause detrimental gradient interference, and develop a simple yet general approach for avoiding ... WebWe propose a form of gradient surgery that projects a task's gradient onto the normal plane of the gradient of any other task that has a conflicting gradient. On a series of … cincinnati public spring break