How can cells in the anterior medial face patch be viewpoint invariant?

In a recent paper, Freiwald and Tsao (2010) found evidence that the responses of cells in the macaque anterior medial (AM) face patch are invariant to significant changes in viewpoint. The monkey subjects had no prior experience with the individuals depicted in the stimuli and were never given an opportunity to view the same individual from different viewpoints sequentially. These results cannot be explained by a mechanism based on temporal association of experienced views. Employing a biologically plausible model of object recognition (software available at cbcl.mit.edu), we show two mechanisms which could account for these results. First, we show that hair style and skin color provide sufficient information to enable viewpoint recognition without resorting to any mechanism that associates images across views. It is likely that a large part of the effect described in patch AM is attributable to these cues. Separately, we show that it is possible to further improve view-invariance using class-specific features (see Vetter 1997). Faces, as a class, transform under 3D rotation in similar enough ways that it is possible to use previously viewed example faces to learn a general model of how all faces rotate. Novel faces can be encoded relative to these previously encountered “template” faces and thus recognized with some degree of invariance to 3D rotation. Since each object class transforms differently under 3D rotation, it follows that invariant recognition from a single view requires a recognition architecture with a detection step determining the class of an object (e.g. face or non-face) prior to a subsequent identification stage utilizing the appropriate class-specific features

Unsupervised learning of clutter-resistant visual representations from natural videos

Populations of neurons in inferotemporal cortex (IT) maintain an explicit code for object identity that also tolerates transformations of object appearance e.g., position, scale, viewing angle [1, 2, 3]. Though the learning rules are not known, recent results [4, 5, 6] suggest the operation of an unsupervised temporal-association-based method e.g., Foldiak's trace rule [7]. Such methods exploit the temporal continuity of the visual world by assuming that visual experience over short timescales will tend to have invariant identity content. Thus, by associating representations of frames from nearby times, a representation that tolerates whatever transformations occurred in the video may be achieved. Many previous studies verified that such rules can work in simple situations without background clutter, but the presence of visual clutter has remained problematic for this approach. Here we show that temporal association based on large class-specific filters (templates) avoids the problem of clutter. Our system learns in an unsupervised way from natural videos gathered from the internet, and is able to perform a difficult unconstrained face recognition task on natural images: Labeled Faces in the Wild [8]

Neurons That Confuse Mirror-Symmetric Object Views

Neurons in inferotemporal cortex that respond similarly to many pairs of mirror-symmetric images -- for example, 45 degree and -45 degree views of the same face -- have often been reported. The phenomenon seemed to be an interesting oddity. However, the same phenomenon has also emerged in simple hierarchical models of the ventral stream. Here we state a theorem characterizing sufficient conditions for this curious invariance to occur in a rather large class of hierarchical networks and demonstrate it with simulations

How Important is Weight Symmetry in Backpropagation?

Gradient backpropagation (BP) requires symmetric feedforward and feedback connections—the same weights must be used for forward and backward passes. This “weight transport problem” [1] is thought to be one of the main reasons of BP’s biological implausibility. Using 15 different classification datasets, we systematically study to what extent BP really depends on weight symmetry. In a study that turned out to be surprisingly similar in spirit to Lillicrap et al.’s demonstration [2] but orthogonal in its results, our experiments indicate that: (1) the magnitudes of feedback weights do not matter to performance (2) the signs of feedback weights do matter—the more concordant signs between feedforward and their corresponding feedback connections, the better (3) with feedback weights having random magnitudes and 100% concordant signs, we were able to achieve the same or even better performance than SGD. (4) some normalizations/stabilizations are indispensable for such asymmetric BP to work, namely Batch Normalization (BN) [3] and/or a “Batch Manhattan” (BM) update rule.This work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF - 1231216

Throwing Down the Visual Intelligence Gauntlet

In recent years, scientific and technological advances have produced artificial systems that have matched or surpassed human capabilities in narrow domains such as face detection and optical character recognition. However, the problem of producing truly intelligent machines still remains far from being solved. In this chapter, we first describe some of these recent advances, and then review one approach to moving beyond these limited successes---the neuromorphic approach of studying and reverse-engineering the networks of neurons in the human brain (specifically, the visual system). Finally, we discuss several possible future directions in the quest for visual intelligence.This research was sponsored by grants from DARPA (IPTO and DSO), National Science Foundation (NSF-0640097, NSF-0827427), AFSOR-THRL (FA8650-05-C-7262). Additional support was provided by: Adobe, Honda Research Institute USA, King Abdullah University Science and Technology grant to B. DeVore, NEC, Sony and especially by the Eugene McDermott Foundation

Learning and disrupting invariance in visual recognition

Learning by temporal association rules such as Foldiak's trace rule is an attractive hypothesis that explains the development of invariance in visual recognition. Consistent with these rules, several recent experiments have shown that invariance can be broken by appropriately altering the visual environment but found puzzling differences in the effects at the psychophysical versus single cell level. We show a) that associative learning provides appropriate invariance in models of object recognition inspired by Hubel and Wiesel b) that we can replicate the "invariance disruption" experiments using these models with a temporal association learning rule to develop and maintain invariance, and c) that we can thereby explain the apparent discrepancies between psychophysics and singe cells effects. We argue that these models account for the stability of perceptual invariance despite the underlying plasticity of the system, the variability of the visual world and expected noise in the biological mechanisms

How important is weight symmetry in backpropagation?

Gradient backpropagation (BP) requires symmetric feedforward and feedback connections-the same weights must be used for forward and backward passes. This "weight transport problem" (Grossberg 1987) is thought to be one of the main reasons to doubt BP's biologically plausibility. Using 15 different classification datasets, we systematically investigate to what extent BP really depends on weight symmetry. In a study that turned out to be surprisingly similar in spirit to Lillicrap et al.'s demonstration (Lillicrap et al. 2014) but orthogonal in its results, our experiments indicate that: (1) the magnitudes of feedback weights do not matter to performance (2) the signs of feedback weights do matter-the more concordant signs between feedforward and their corresponding feedback connections, the better (3) with feedback weights having random magnitudes and 100% concordant signs, we were able to achieve the same or even better performance than SGD. (4) some normalizations/stabilizations are indispensable for such asymmetric BP to work, namely Batch Normalization (BN) (Ioffe and Szegedy 2015) and/or a "Batch Manhattan" (BM) update rule.National Science Foundation (U.S.) (STC Award CCF 1231216

Learning invariant representations and applications to face verification

One approach to computer object recognition and modeling the brain's ventral stream involves unsupervised learning of representations that are invariant to common transformations. However, applications of these ideas have usually been limited to 2D affine transformations, e.g., translation and scaling, since they are easiest to solve via convolution. In accord with a recent theory of transformation-invariance, we propose a model that, while capturing other common convolutional networks as special cases, can also be used with arbitrary identity-preserving transformations. The model's wiring can be learned from videos of transforming objects---or any other grouping of images into sets by their depicted object. Through a series of successively more complex empirical tests, we study the invariance/discriminability properties of this model with respect to different transformations. First, we empirically confirm theoretical predictions for the case of 2D affine transformations. Next, we apply the model to non-affine transformations: as expected, it performs well on face verification tasks requiring invariance to the relatively smooth transformations of 3D rotation-in-depth and changes in illumination direction. Surprisingly, it can also tolerate clutter transformations'' which map an image of a face on one background to an image of the same face on a different background. Motivated by these empirical findings, we tested the same model on face verification benchmark tasks from the computer vision literature: Labeled Faces in the Wild, PubFig and a new dataset we gathered---achieving strong performance in these highly unconstrained cases as well.