10,449 research outputs found
Learning to Associate Words and Images Using a Large-scale Graph
We develop an approach for unsupervised learning of associations between
co-occurring perceptual events using a large graph. We applied this approach to
successfully solve the image captcha of China's railroad system. The approach
is based on the principle of suspicious coincidence. In this particular
problem, a user is presented with a deformed picture of a Chinese phrase and
eight low-resolution images. They must quickly select the relevant images in
order to purchase their train tickets. This problem presents several
challenges: (1) the teaching labels for both the Chinese phrases and the images
were not available for supervised learning, (2) no pre-trained deep
convolutional neural networks are available for recognizing these Chinese
phrases or the presented images, and (3) each captcha must be solved within a
few seconds. We collected 2.6 million captchas, with 2.6 million deformed
Chinese phrases and over 21 million images. From these data, we constructed an
association graph, composed of over 6 million vertices, and linked these
vertices based on co-occurrence information and feature similarity between
pairs of images. We then trained a deep convolutional neural network to learn a
projection of the Chinese phrases onto a 230-dimensional latent space. Using
label propagation, we computed the likelihood of each of the eight images
conditioned on the latent space projection of the deformed phrase for each
captcha. The resulting system solved captchas with 77% accuracy in 2 seconds on
average. Our work, in answering this practical challenge, illustrates the power
of this class of unsupervised association learning techniques, which may be
related to the brain's general strategy for associating language stimuli with
visual objects on the principle of suspicious coincidence.Comment: 8 pages, 7 figures, 14th Conference on Computer and Robot Vision 201
GRASS: Generative Recursive Autoencoders for Shape Structures
We introduce a novel neural network architecture for encoding and synthesis
of 3D shapes, particularly their structures. Our key insight is that 3D shapes
are effectively characterized by their hierarchical organization of parts,
which reflects fundamental intra-shape relationships such as adjacency and
symmetry. We develop a recursive neural net (RvNN) based autoencoder to map a
flat, unlabeled, arbitrary part layout to a compact code. The code effectively
captures hierarchical structures of man-made 3D objects of varying structural
complexities despite being fixed-dimensional: an associated decoder maps a code
back to a full hierarchy. The learned bidirectional mapping is further tuned
using an adversarial setup to yield a generative model of plausible structures,
from which novel structures can be sampled. Finally, our structure synthesis
framework is augmented by a second trained module that produces fine-grained
part geometry, conditioned on global and local structural context, leading to a
full generative pipeline for 3D shapes. We demonstrate that without
supervision, our network learns meaningful structural hierarchies adhering to
perceptual grouping principles, produces compact codes which enable
applications such as shape classification and partial matching, and supports
shape synthesis and interpolation with significant variations in topology and
geometry.Comment: Corresponding author: Kai Xu ([email protected]
Empiricism without Magic: Transformational Abstraction in Deep Convolutional Neural Networks
In artificial intelligence, recent research has demonstrated the remarkable potential of Deep Convolutional Neural Networks (DCNNs), which seem to exceed state-of-the-art performance in new domains weekly, especially on the sorts of very difficult perceptual discrimination tasks that skeptics thought would remain beyond the reach of artificial intelligence. However, it has proven difficult to explain why DCNNs perform so well. In philosophy of mind, empiricists have long suggested that complex cognition is based on information derived from sensory experience, often appealing to a faculty of abstraction. Rationalists have frequently complained, however, that empiricists never adequately explained how this faculty of abstraction actually works. In this paper, I tie these two questions together, to the mutual benefit of both disciplines. I argue that the architectural features that distinguish DCNNs from earlier neural networks allow them to implement a form of hierarchical processing that I call “transformational abstraction”. Transformational abstraction iteratively converts sensory-based representations of category exemplars into new formats that are increasingly tolerant to “nuisance variation” in input. Reflecting upon the way that DCNNs leverage a combination of linear and non-linear processing to efficiently accomplish this feat allows us to understand how the brain is capable of bi-directional travel between exemplars and abstractions, addressing longstanding problems in empiricist philosophy of mind. I end by considering the prospects for future research on DCNNs, arguing that rather than simply implementing 80s connectionism with more brute-force computation, transformational abstraction counts as a qualitatively distinct form of processing ripe with philosophical and psychological significance, because it is significantly better suited to depict the generic mechanism responsible for this important kind of psychological processing in the brain
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