Humans quickly and accurately learn new visual concepts from sparse data, sometimes just a single example. The impressive performance of artificial neural networks which hierarchically pool afferents across scales and positions suggests that the hierarchical organization of the human visual system is critical to its accuracy. These approaches, however, require magnitudes of order more examples than human learners. We used a benchmark deep learning model to show that the hierarchy can also be leveraged to vastly improve the speed of learning. We specifically show how previously learned but broadly tuned conceptual representations can be used to learn visual concepts from as few as two positive examples; reusing visual representations from earlier in the visual hierarchy, as in prior approaches, requires significantly more examples to perform comparably. These results suggest techniques for learning even more efficiently and provide a biologically plausible way to learn new visual concepts from few examples.
Humans have the remarkable ability to quickly learn new concepts from sparse data. Preschoolers, for example, can acquire and use new words on the basis of sometimes just a single example (Carey and Bartlett, 1978), and adults can reliably discriminate and name new categories after just one or two training trials (Coutanche and Thompson-Schill, 2014, 2015b; Lake et al., 2015). Given that principled generalization is impossible without leveraging prior knowledge (Watanabe, 1969), this impressive performance raises the question of how the brain might use prior knowledge to establish new concepts from such sparse data.
Several decades of anatomical, computational, and experimental work suggest that the brain builds a representation of the visual world by way of the so-called ventral visual stream, along which information is processed by a simple-to-complex hierarchy up to neurons in ventral temporal cortex that are selective for complex objects such as faces, objects and words (Kravitz et al., 2013). According to computational models (Nosofsky, 1986; Riesenhuber and Poggio, 2000; Thomas et al., 2001; Freedman et al., 2003; Ashby and Spiering, 2004) as well as human functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) studies (Jiang et al., 2007; Scholl et al., 2014), these object-selective neurons in high-level visual cortex can then provide input to downstream cortical areas, such as prefrontal cortex (PFC) and the anterior temporal lobe (ATL), to mediate the identification, discrimination, or categorization of stimuli, as well as more broadly throughout cortex for task-specific needs (Hebart et al., 2018). It is at this level where these theories of object categorization in the brain connect with influential theories of semantic cognition that have proposed that the ATL may act as a semantic hub (Ralph et al., 2017), based on neuropsychological findings (Hodges et al., 2000; Mion et al., 2010; Jefferies, 2013) and studies that have used fMRI (Vandenberghe et al., 1996; Coutanche and Thompson-Schill, 2015a; Malone et al., 2016; Chen et al., 2017) or intracranial EEG (iEEG; Chan et al., 2011) to decode category representations in the anteroventral temporal lobe.
Computational work suggests that hierarchical structure is a key architectural feature of the ventral stream for flexibly learning novel recognition tasks (Poggio, 2012). For instance, the increasing tolerance to scaling and translation in progressively higher layers of the processing hierarchy due to pooling of afferents preferring the same feature across scales and positions supports robust learning of novel object recognition tasks by reducing the problem’s sample complexity (Poggio, 2012). Indeed, computational models based on this hierarchical structure, such as the HMAX model (Riesenhuber and Poggio, 1999) and, more recently, convolutional neural network (CNN)-based approaches have been shown to achieve human-like performance in object recognition tasks given sufficient numbers of training examples (Jiang et al., 2006; Serre et al., 2007a; Crouzet and Serre, 2011; Yamins et al., 2013, 2014) and even to accurately predict human neural activity (Schrimpf et al., 2018).
In addition to their invariance properties, the complex shape selectivity of intermediate features in the brain, e.g., in V4 or posterior inferotemporal cortex (IT), is thought to span a feature space well-matched to the appearance of objects in the natural world (Serre et al., 2007a; Yamins et al., 2014). Indeed, it has been shown that reusing the same intermediate features permits the efficient learning of novel recognition tasks (Serre et al., 2007a; Donahue et al., 2013; Oquab et al., 2014; Razavian et al., 2014; Yosinski et al., 2014), and the reuse of existing representations at different levels of the object processing hierarchy is at the core of models of hierarchical learning in the brain (Ahissar and Hochstein, 2004). These theories and prior computational work are limited, however, to re use of existing representations at the level of objects and below. Yet, as mentioned before, processing hierarchies in the brain do not end at the object-level but extend to the level of concepts and beyond, e.g., in the ATL, downstream from object-level representations in IT. These representations are importantly different from the earlier visual representations, generalizing over exemplars to support category-sensitive behavior at the expense of exemplar-specific details (Bankson et al., 2018). Intuitively, leveraging these previously learned visual concept representations could substantially facilitate the learning of novel concepts, along the lines of “a platypus looks a bit like a duck, a beaver, and a sea otter.” In fact, there is intriguing evidence that the brain might leverage existing concept representations to facilitate the learning of novel concepts: in fast mapping (Carey and Bartlett, 1978; Coutanche and Thompson-Schill, 2014, 2015b), a novel concept is inferred from a single example by contrasting it with a related but already known concept, both of which are relevant to answering some query. Fast mapping is more generally consistent with the intuition that the relationships between concepts and categories are crucial to understanding the concepts themselves (Miller and Johnson-Laird, 1976; Woods, 1981; Carey, 1985, 2009). The brain’s ability to quickly master new visual categories may then depend on the size and scope of the bank of visual categories it has already mastered. Indeed, it has been posited that the brain’s ability to perform fast mapping might depend on its ability to relate the new knowledge to existing schemas in the ATL (Sharon et al., 2011). Yet, there is no computational demonstration that such leveraging of prior learning can indeed facilitate the learning of novel concepts. Showing that leveraging existing concept representations can dramatically reduce the number of examples needed to learn novel concepts would not only provide an explanation for the brain’s superior ability to learn novel concepts from few examples, but would also be of significant interest for artificial intelligence, given that current deep learning systems still require substantially more training examples to reach human-like performance (Lake et al., 2017; Schrimpf et al., 2018).
We show that leveraging prior learning at the concept level in a benchmark deep learning model leads to vastly improved abilities to learn from few examples. While visual learning and reasoning involves a wide variety of skills—including memory (Brady et al., 2008, 2011), compositional reasoning (Lake et al., 2015; Overlan et al., 2017), and multimodal integration (Yildirim and Jacobs, 2013, 2015)—we focus here on the task of object recognition. This ability to classify visual stimuli into categories is a key skill underlying many of our other visual abilities. We specifically find that broadly tuned conceptual representations can be used to learn visual concepts from as few as two positive examples, accurately discriminating positive examples of the concept from a wide variety of negative examples; visual representations from earlier in the visual hierarchy require significantly more examples to reach comparable levels of performance.