In this section, we detail the model architectures examined in the experiments and list all hyperpa-rameters used in the experiments. Both architectures consist of five stages, each consisting of a combination of convolutional layers with ReLU activation and max pooling layers. The base number of channels in consecutive stages for VGG architectures equals 64, 128, 256, 512, and 512. The subsequent stages are composed of residual blocks. In the case of ResNets, we report the results for the'conv2' layers.

This paper introduces the first generalization and adaptation benchmark using machine learning for evaluating out-of-distribution performance of electromyography (EMG) classification algorithms. The ability of an EMG classifier to handle inputs drawn from a different distribution than the training distribution is critical for real-world deployment as a control interface. By predicting the user's intended gesture using EMG signals, we can create a wearable solution to control assistive technologies, such as computers, prosthetics, and mobile manipulator robots. This new out-of-distribution benchmark consists of two major tasks that have utility for building robust and adaptable control interfaces: 1) intersubject classification, and 2) adaptation using train-test splits for time-series.

The ability of a document classifier to handle inputs that are drawn from a distribution different from the training distribution is crucial for robust deployment and generalizability. The RVL-CDIP corpus is the de facto standard benchmark for document classification, yet to our knowledge all studies that use this corpus do not include evaluation on out-of-distribution documents. In this paper, we curate and release a new out-of-distribution benchmark for evaluating out-of-distribution performance for document classifiers. Our new out-of-distribution benchmark consists of two types of documents: those that are not part of any of the 16 in-domain RVL-CDIP categories (RVL-CDIP-O), and those that are one of the 16 in-domain categories yet are drawn from a distribution different from that of the original RVL-CDIP dataset (RVL-CDIP-N). While prior work on document classification for in-domain RVL-CDIP documents reports high accuracy scores, we find that these models exhibit accuracy drops of between roughly 15-30% on our new out-of-domain RVL-CDIP-N benchmark, and further struggle to distinguish between in-domain RVL-CDIP-N and out-of-domain RVL-CDIP-O inputs. Our new benchmark provides researchers with a valuable new resource for analyzing out-of-distribution performance on document classifiers.

We present an approach that eliminates this problem.

The RVL-CDIP corpus has emerged as the de facto benchmark dataset for document classification. This single-label classification dataset covers 16 document categories (e.g.,

In this section, we detail the model architectures examined in the experiments and list all hyperpa-rameters used in the experiments. Both architectures consist of five stages, each consisting of a combination of convolutional layers with ReLU activation and max pooling layers. The base number of channels in consecutive stages for VGG architectures equals 64, 128, 256, 512, and 512. The subsequent stages are composed of residual blocks. In the case of ResNets, we report the results for the'conv2' layers.

We explore the tunnel's behavior through comprehensive empirical

We present an approach that eliminates this problem.