Diffusion Models as Masked Audio-Video Learners

Over the past several years, the synchronization between audio and visual signals has been leveraged to learn richer audio-visual representations. Aided by the large availability of unlabeled videos, many unsupervised training frameworks have demonstrated impressive results in various downstream audio and video tasks. Recently, Masked Audio-Video Learners (MAViL) has emerged as a state-of-the-art audio-video pre-training framework. MAViL couples contrastive learning with masked autoencoding to jointly reconstruct audio spectrograms and video frames by fusing information from both modalities. In this paper, we study the potential synergy between diffusion models and MAViL, seeking to derive mutual benefits from these two frameworks. The incorporation of diffusion into MAViL, combined with various training efficiency methodologies that include the utilization of a masking ratio curriculum and adaptive batch sizing, results in a notable 32% reduction in pre-training Floating-Point Operations (FLOPS) and an 18% decrease in pre-training wall clock time. Crucially, this enhanced efficiency does not compromise the model's performance in downstream audio-classification tasks when compared to MAViL's performance.

Layer-Wise Data-Free CNN Compression

We present an efficient method for compressing a trained neural network without using any data. Our data-free method requires 14x-450x fewer FLOPs than comparable state-of-the-art methods. We break the problem of data-free network compression into a number of independent layer-wise compressions. We show how to efficiently generate layer-wise training data, and how to precondition the network to maintain accuracy during layer-wise compression. We show state-of-the-art performance on MobileNetV1 for data-free low-bit-width quantization. We also show state-of-the-art performance on data-free pruning of EfficientNet B0 when combining our method with end-to-end generative methods.