5 research outputs found
The Neural Representation Benchmark and its Evaluation on Brain and Machine
A key requirement for the development of effective learning representations
is their evaluation and comparison to representations we know to be effective.
In natural sensory domains, the community has viewed the brain as a source of
inspiration and as an implicit benchmark for success. However, it has not been
possible to directly test representational learning algorithms directly against
the representations contained in neural systems. Here, we propose a new
benchmark for visual representations on which we have directly tested the
neural representation in multiple visual cortical areas in macaque (utilizing
data from [Majaj et al., 2012]), and on which any computer vision algorithm
that produces a feature space can be tested. The benchmark measures the
effectiveness of the neural or machine representation by computing the
classification loss on the ordered eigendecomposition of a kernel matrix
[Montavon et al., 2011]. In our analysis we find that the neural representation
in visual area IT is superior to visual area V4. In our analysis of
representational learning algorithms, we find that three-layer models approach
the representational performance of V4 and the algorithm in [Le et al., 2012]
surpasses the performance of V4. Impressively, we find that a recent supervised
algorithm [Krizhevsky et al., 2012] achieves performance comparable to that of
IT for an intermediate level of image variation difficulty, and surpasses IT at
a higher difficulty level. We believe this result represents a major milestone:
it is the first learning algorithm we have found that exceeds our current
estimate of IT representation performance. We hope that this benchmark will
assist the community in matching the representational performance of visual
cortex and will serve as an initial rallying point for further correspondence
between representations derived in brains and machines.Comment: The v1 version contained incorrectly computed kernel analysis curves
and KA-AUC values for V4, IT, and the HT-L3 models. They have been corrected
in this versio