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We designed our benchmark to reward qualities seen in human vision: (1)Context dependence (2)Analogy Making (3)Few-shot learning with infinite vocabulary. Simply overfitting and memorization by #DeepLearning will not be enough to do well on our benchmark.
Context dependence: Same shape will have different meanings in different contexts. In left figure, highlighted shape shares a different property with other 5 positive examples, compared to right. Hence, simple pattern matching, which #DeepLearning is good at, WILL FAIL.pic.twitter.com/i8E0dOeydk
Analogy making or compositionality: Simple shapes compose together. E.g. small circles in highlighted figure are arranged together to form a "meta" shape. Humans are amazing at creating abstractions. We want to test this ability in #AIpic.twitter.com/Lz4ouU4Y9a
Infinite vocabulary: Previous benchmarks are limited to finite categories, and it is easy for #DeepLearning to memorize. To prevent this, we programmatically generate new concepts. We have subcategories: free-form, basic and abstract.pic.twitter.com/vVVi8rTCFR
Our dataset looks so simple and toy-like, yet deeply challenging for #DeepLearning due to (1)context dependence (2)abstractions (3) infinite vocabulary. We resorted to synthetic data due to severe data imbalance and scarcity with real datasets for few-shot learning.
On our benchmark, a basic neuro-symbolic method beat all neural approaches consistently and significantly. Neural approaches include latest meta/few-shot/self-supervised approaches. Shows symbol grounding is fundamental to concept learning.
Project page for our @NeurIPSConf Bongard-LOGO paper with all resources including code for dataset generation. @yukez @ZhidingYu @wn8_nie @abp4_ankit @NVIDIAAI https://research.nvidia.com/publication/2020-12_Bongard-LOGO …
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