  The advancement in computing power has significantly reduced the training
times for deep learning, fostering the rapid development of networks designed
for object recognition. However, the exploration of object utility, which is
the affordance of the object, as opposed to object recognition, has received
comparatively less attention. This work focuses on the problem of exploration
of object affordances using existing networks trained on the object
classification dataset. While pre-trained networks have proven to be
instrumental in transfer learning for classification tasks, this work diverges
from conventional object classification methods. Instead, it employs
pre-trained networks to discern affordance labels without the need for
specialized layers, abstaining from modifying the final layers through the
addition of classification layers. To facilitate the determination of
affordance labels without such modifications, two approaches, i.e. subspace
clustering and manifold curvature methods are tested. These methods offer a
distinct perspective on affordance label recognition. Especially, manifold
curvature method has been successfully tested with nine distinct pre-trained
networks, each achieving an accuracy exceeding 95%. Moreover, it is observed
that manifold curvature and subspace clustering methods explore affordance
labels that are not marked in the ground truth, but object affords in various
cases.
