Embracing the Power of Known Class Bias in Open Set Recognition From a Reconstruction Perspective

The open set known class bias is conventionally viewed as a fatal problem i.e., the models trained solely on known classes tend to fit unknown classes to known classes with high confidence in inference. Thus existing methods, without exception make a choice in two manners: most methods opt for eliminating the known class bias as much as possible with tireless efforts, while others circumvent the known class bias by employing a reconstruction method. However, in this paper, we challenge the two widely accepted approaches and present a novel proposition: the so-called harmful known class bias for most methods is, exactly conversely, beneficial for the reconstruction-based method and thus such known class bias can serve as a positive-incentive to the Open set recognition (OSR) models from a reconstruction perspective. Along this line, we propose the Bias Enhanced Reconstruction Learning (BERL) framework to enhance the known class bias respectively from the class level, model level and sample level. Specifically, at the class level, a specific representation is constructed in a supervised contrastive manner to avoid overgeneralization, while a diffusion model is employed by injecting the class prior to guide the biased reconstruction at the model level. Additionally, we leverage the advantages of the diffusion model to design a self-adaptive strategy, enabling effective sample-level biased sampling based on the information bottleneck theory. Experiments on various benchmarks demonstrate the effectiveness and performance superiority of the proposed method.