MemGEN: Memory is All You Need
This proposes a potentially revolutionary paradigm for machine learning, though it appears incremental as it builds on known memorization concepts.
The authors introduced Deep Memory, a new learning paradigm based on the 'Learning By Heart' principle, and applied it to generative modeling across images, natural language, and text, achieving generated samples indistinguishable from training examples in experiments.
We propose a new learning paradigm called Deep Memory. It has the potential to completely revolutionize the Machine Learning field. Surprisingly, this paradigm has not been reinvented yet, unlike Deep Learning. At the core of this approach is the \textit{Learning By Heart} principle, well studied in primary schools all over the world. Inspired by poem recitation, or by $π$ decimal memorization, we propose a concrete algorithm that mimics human behavior. We implement this paradigm on the task of generative modeling, and apply to images, natural language and even the $π$ decimals as long as one can print them as text. The proposed algorithm even generated this paper, in a one-shot learning setting. In carefully designed experiments, we show that the generated samples are indistinguishable from the training examples, as measured by any statistical tests or metrics.