  An evaluation criterion for safe and trustworthy deep learning is how well
the invariances captured by representations of deep neural networks (DNNs) are
shared with humans. We identify challenges in measuring these invariances.
Prior works used gradient-based methods to generate identically represented
inputs (IRIs), ie, inputs which have identical representations (on a given
layer) of a neural network, and thus capture invariances of a given network.
One necessary criterion for a network's invariances to align with human
perception is for its IRIs look 'similar' to humans. Prior works, however, have
mixed takeaways; some argue that later layers of DNNs do not learn human-like
invariances (\cite{jenelle2019metamers}) yet others seem to indicate otherwise
(\cite{mahendran2014understanding}). We argue that the loss function used to
generate IRIs can heavily affect takeaways about invariances of the network and
is the primary reason for these conflicting findings. We propose an adversarial
regularizer on the IRI generation loss that finds IRIs that make any model
appear to have very little shared invariance with humans. Based on this
evidence, we argue that there is scope for improving models to have human-like
invariances, and further, to have meaningful comparisons between models one
should use IRIs generated using the regularizer-free loss. We then conduct an
in-depth investigation of how different components (eg architectures, training
losses, data augmentations) of the deep learning pipeline contribute to
learning models that have good alignment with humans. We find that
architectures with residual connections trained using a (self-supervised)
contrastive loss with $\ell_p$ ball adversarial data augmentation tend to learn
invariances that are most aligned with humans. Code:
\url{github.com/nvedant07/Human-NN-Alignment}.
