  Data-free quantization (DFQ) recovers the performance of quantized network
(Q) without accessing the real data, but generates the fake sample via a
generator (G) by learning from full-precision network (P) instead. However,
such sample generation process is totally independent of Q, specialized as
failing to consider the adaptability of the generated samples, i.e., beneficial
or adversarial, over the learning process of Q, resulting into non-ignorable
performance loss. Building on this, several crucial questions -- how to measure
and exploit the sample adaptability to Q under varied bit-width scenarios? how
to generate the samples with desirable adaptability to benefit the quantized
network? -- impel us to revisit DFQ. In this paper, we answer the above
questions from a game-theory perspective to specialize DFQ as a zero-sum game
between two players -- a generator and a quantized network, and further propose
an Adaptability-aware Sample Generation (AdaSG) method. Technically, AdaSG
reformulates DFQ as a dynamic maximization-vs-minimization game process
anchored on the sample adaptability. The maximization process aims to generate
the sample with desirable adaptability, such sample adaptability is further
reduced by the minimization process after calibrating Q for performance
recovery. The Balance Gap is defined to guide the stationarity of the game
process to maximally benefit Q. The theoretical analysis and empirical studies
verify the superiority of AdaSG over the state-of-the-arts. Our code is
available at https://github.com/hfutqian/AdaSG.
