  Costly, noisy, and over-specialized, labels are to be set aside in favor of
unsupervised learning if we hope to learn cheap, reliable, and transferable
models. To that end, spectral embedding, self-supervised learning, or
generative modeling have offered competitive solutions. Those methods however
come with numerous challenges \textit{e.g.} estimating geodesic distances,
specifying projector architectures and anti-collapse losses, or specifying
decoder architectures and reconstruction losses. In contrast, we introduce a
simple explainable alternative -- coined \textbf{DIET} -- to learn
representations from unlabeled data, free of those challenges. \textbf{DIET} is
blatantly simple: take one's favorite classification setup and use the
\textbf{D}atum \textbf{I}nd\textbf{E}x as its \textbf{T}arget class,
\textit{i.e. each sample is its own class}, no further changes needed.
\textbf{DIET} works without a decoder/projector network, is not based on
positive pairs nor reconstruction, introduces no hyper-parameters, and works
out-of-the-box across datasets and architectures. Despite \textbf{DIET}'s
simplicity, the learned representations are of high-quality and often on-par
with the state-of-the-art \textit{e.g.} using a linear classifier on top of
DIET's learned representation reaches $71.4\%$ on CIFAR100 with a Resnet101,
$52.5\%$ on TinyImagenet with a Resnext50.
