  This paper introduces RDA, a pioneering approach designed to address two
primary deficiencies prevalent in previous endeavors aiming at stealing
pre-trained encoders: (1) suboptimal performances attributed to biased
optimization objectives, and (2) elevated query costs stemming from the
end-to-end paradigm that necessitates querying the target encoder every epoch.
Specifically, we initially Refine the representations of the target encoder for
each training sample, thereby establishing a less biased optimization objective
before the steal-training phase. This is accomplished via a sample-wise
prototype, which consolidates the target encoder's representations for a given
sample's various perspectives. Demanding exponentially fewer queries compared
to the end-to-end approach, prototypes can be instantiated to guide subsequent
query-free training. For more potent efficacy, we develop a multi-relational
extraction loss that trains the surrogate encoder to Discriminate mismatched
embedding-prototype pairs while Aligning those matched ones in terms of both
amplitude and angle. In this way, the trained surrogate encoder achieves
state-of-the-art results across the board in various downstream datasets with
limited queries. Moreover, RDA is shown to be robust to multiple widely-used
defenses.
