7,827 research outputs found
Adversarial Semantic Scene Completion from a Single Depth Image
We propose a method to reconstruct, complete and semantically label a 3D
scene from a single input depth image. We improve the accuracy of the regressed
semantic 3D maps by a novel architecture based on adversarial learning. In
particular, we suggest using multiple adversarial loss terms that not only
enforce realistic outputs with respect to the ground truth, but also an
effective embedding of the internal features. This is done by correlating the
latent features of the encoder working on partial 2.5D data with the latent
features extracted from a variational 3D auto-encoder trained to reconstruct
the complete semantic scene. In addition, differently from other approaches
that operate entirely through 3D convolutions, at test time we retain the
original 2.5D structure of the input during downsampling to improve the
effectiveness of the internal representation of our model. We test our approach
on the main benchmark datasets for semantic scene completion to qualitatively
and quantitatively assess the effectiveness of our proposal.Comment: 2018 International Conference on 3D Vision (3DV
sk_p: a neural program corrector for MOOCs
We present a novel technique for automatic program correction in MOOCs,
capable of fixing both syntactic and semantic errors without manual, problem
specific correction strategies. Given an incorrect student program, it
generates candidate programs from a distribution of likely corrections, and
checks each candidate for correctness against a test suite.
The key observation is that in MOOCs many programs share similar code
fragments, and the seq2seq neural network model, used in the natural-language
processing task of machine translation, can be modified and trained to recover
these fragments.
Experiment shows our scheme can correct 29% of all incorrect submissions and
out-performs state of the art approach which requires manual, problem specific
correction strategies
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