127 citations · 364 across the 15 of their papers we have counts for
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cs.CV2019★ 96 cited
More Is Less: Learning Efficient Video Representations by Big-Little Network and Depthwise Temporal Aggregation
Quanfu Fan, Chun-Fu Chen, Hilde Kuehne +2
Current state-of-the-art models for video action recognition are mostly based on expensive 3D ConvNets. This results in a need for large GPU clusters to train and evaluate such arc…
stat.ML2019
SimVAE: Simulator-Assisted Training forInterpretable Generative Models
Akash Srivastava, Jessie Rosenberg, Dan Gutfreund +1
This paper presents a simulator-assisted training method (SimVAE) for variational autoencoders (VAE) that leads to a disentangled and interpretable latent space. Training SimVAE is…