Revealing Distributional Vulnerability of Explicit Discriminators by Implicit Generators

An explicit discriminator trained on observable in-distribution (ID) samples can make high-confidence prediction on out-of-distribution (OOD) samples due to its distributional vulnerability. This is primarily caused by the limited ID samples observable for training discriminators when OOD samples are unavailable. To address this issue, the state-of-the-art methods train the discriminator with OOD samples generated by general assumptions without considering the data and network characteristics. However, different network architectures and training ID datasets may cause diverse vulnerabilities, and the generated OOD samples thus usually misaddress the specific distributional vulnerability of the explicit discriminator. To reveal and patch the distributional vulnerabilities, we propose a novel method of \textit{fine-tuning explicit discriminators by implicit generators} (FIG). According to the Shannon entropy, an explicit discriminator can construct its corresponding implicit generator to generate specific OOD samples without extra training costs. A Langevin Dynamic sampler then draws high-quality OOD samples from the generator to reveal the vulnerability. Finally, a regularizer, constructed according to the design principle of the implicit generator, patches the distributional vulnerability by encouraging those generated OOD samples with high entropy. Our experiments on four networks, four ID datasets and seven OOD datasets demonstrate that FIG achieves state-of-the-art OOD detection performance and maintains a competitive classification capability.