LORS: Low-rank Residual Structure for Parameter-Efficient Network Stacking

Deep learning models particularly those based on transformers often employ numerous stacked structures which possess identical architectures and perform similar functions. While effective this stacking paradigm leads to a substantial increase in the number of parameters pos- ing challenges for practical applications. In today's land- scape of increasingly large models stacking depth can even reach dozens further exacerbating this issue. To miti- gate this problem we introduce LORS (LOw-rank Residual Structure). LORS allows stacked modules to share the majority of parameters requiring a much smaller num- ber of unique ones per module to match or even surpass the performance of using entirely distinct ones thereby significantly reducing parameter usage. We validate our method by applying it to the stacked decoders of a query- based object detector and conduct extensive experiments on the widely used MS COCO dataset. Experimental re- sults demonstrate the effectiveness of our method as even with a 70% reduction in the parameters of the decoder our method still enables the model to achieve comparable or even better performance than its original.