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Transformers have rapidly increased in popularity in recent years, achievingstate-of-the-art performance in processing text, images, audio and video.However, Transformers present large computational requirements for bothtraining and inference, and are prone to overfitting during training. Toaddress these challenges, we present Input Compression with PositionalConsistency (ICPC), a new data augmentation method that, unlike prioraugmentation techniques, simultaneously improves both generalization andtraining efficiency. ICPC applies varying levels of compression to eachtraining sample in each epoch. This leads to smaller input sequences beingprocessed by the Transformer, and hence faster training, while also alleviatingoverfitting by presenting each input with different compression levels. Weintroduce a consistency-aware position selection method in ICPC that enablesaccurate processing of compressed inputs without any changes to the underlyingTransformer architecture. We detail compression-based augmentation methods forfour different modalities -- insignificant word pruning for text, resolutionmodulation for images, spatio-temporal resolution modulation for videos, andspectogram size modulation for audio. ICPC also enables efficientvariable-effort inference, where samples are first inferred at high compressionlevels, and progressively re-evaluated with lower compression for morechallenging inputs. On 9 diverse tasks spanning 4 different modalities, ICPCimproves accuracy by up to 1%, while also accelerating training and inferenceby up to 2.9X and 2.6X, respectively. Code is available athttps://github.com/amrnag/ICPC.

Input Compression with Positional Consistency for Efficient Training and Inference of Transformer Neural Networks