ReLU6

ReLU6 is a variant of the Rectified Linear Unit (ReLU) activation function used in artificial neural networks. Standard ReLU is defined as f(x) = max(0, x). ReLU6 modifies this by introducing an upper bound, making it f(x) = min(max(0, x), 6). This means that for any input x, the output of ReLU6 will be at least 0 and at most 6.

The primary motivation behind ReLU6 is to improve the performance of networks operating in low-precision environments, particularly those that will be deployed on mobile devices or embedded systems. By capping the output at 6, ReLU6 helps to prevent extremely large activation values from occurring. These large values can lead to numerical instability and require higher precision representations, which are undesirable in resource-constrained settings.

The bounded nature of ReLU6 can also have a regularizing effect, potentially reducing overfitting. By limiting the range of activations, it can make the network less sensitive to outliers in the input data. This property is particularly useful in convolutional neural networks (CNNs) for tasks like image recognition. ReLU6 is often used in conjunction with techniques like quantization-aware training to further optimize models for efficient inference.