Boost Test-Time Performance with Closed-Loop Inference

• URL: http://arxiv.org/abs/2203.10853v1
• Date: Mon, 21 Mar 2022 10:20:21 GMT
• Title: Boost Test-Time Performance with Closed-Loop Inference
• Authors: Shuaicheng Niu and Jiaxiang Wu and Yifan Zhang and Guanghui Xu and Haokun Li and Junzhou Huang and Yaowei Wang and Mingkui Tan
• Abstract summary: We propose to predict hard-classified test samples in a looped manner to boost the model performance. We first devise a filtering criterion to identify those hard-classified test samples that need additional inference loops. For each hard sample, we construct an additional auxiliary learning task based on its original top-$K$ predictions to calibrate the model.
• Score: 85.43516360332646
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Conventional deep models predict a test sample with a single forward propagation, which, however, may not be sufficient for predicting hard-classified samples. On the contrary, we human beings may need to carefully check the sample many times before making a final decision. During the recheck process, one may refine/adjust the prediction by referring to related samples. Motivated by this, we propose to predict those hard-classified test samples in a looped manner to boost the model performance. However, this idea may pose a critical challenge: how to construct looped inference, so that the original erroneous predictions on these hard test samples can be corrected with little additional effort. To address this, we propose a general Closed-Loop Inference (CLI) method. Specifically, we first devise a filtering criterion to identify those hard-classified test samples that need additional inference loops. For each hard sample, we construct an additional auxiliary learning task based on its original top-$K$ predictions to calibrate the model, and then use the calibrated model to obtain the final prediction. Promising results on ImageNet (in-distribution test samples) and ImageNet-C (out-of-distribution test samples) demonstrate the effectiveness of CLI in improving the performance of any pre-trained model.

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