As the learned attention mechanisms tend to use few of these edges, such high-degree connections may be unnecessary.

Description-level outliers include class-relevant information, but when the class label is omitted, they become very vague and difficult to interpret.

In this work, we present a dual-stage low-bit post-training quantization (PTQ) method for image super-resolution, namely 2DQuant, which achieves efficient and accurate SR under low-bit quantization.

This leads to a new MU paradigm, termed prune first, then unlearn, which infuses a sparse model prior into the unlearning process.