LRP is NOT applicable for models with only ReLU activations and max pooling nonlinearities.a.Falseb.True

Step 1: Break Down the Problem

We need to determine whether the statement regarding LRP (Layer-wise Relevance Propagation), ReLU (Rectified Linear Unit) activations, and max pooling nonlinearities is true or false.

Step 2: Relevant Concepts

LRP is a technique used to assign relevance scores to the input features of a neural network based on the activations of the network layers. It may struggle with certain activation functions or structures within the network.

Step 3: Analysis and Detail

1. ReLU Activation:

   • ReLU outputs zero for any negative input, which can lead to loss of information.
   • Upon using LRP, the relevance propagation can be problematic due to the zeroing behavior of ReLU, causing difficulties in backpropagating relevance.

2. Max Pooling Nonlinearities:

   • Max pooling layers further compress information by taking the maximum value in a region, potentially losing context about the other values.
   • LRP relies on the full distribution of activations, and max pooling might hinder the propagation of relevance due to non-smooth mappings.

Step 4: Verify and Summarize

Lack of applicability for LRP in networks formed solely by ReLU activations and max pooling suggests that important characteristics and patterns may be lost, making relevance unclear or difficult to ascertain.

Final Answer

The statement is b. True; LRP is not applicable for models that consist only of ReLU activations and max pooling nonlinearities due to the challenges in relevance propagation.