Timezone: »

 
Poster
One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers
Ari Morcos · Haonan Yu · Michela Paganini · Yuandong Tian

Tue Dec 10 10:45 AM -- 12:45 PM (PST) @ East Exhibition Hall B + C #170
in Deep Learning -- Visualization or Exposition Techniques for Deep Networks »

The success of lottery ticket initializations (Frankle and Carbin, 2019) suggests that small, sparsified networks can be trained so long as the network is initialized appropriately. Unfortunately, finding these "winning ticket'' initializations is computationally expensive. One potential solution is to reuse the same winning tickets across a variety of datasets and optimizers. However, the generality of winning ticket initializations remains unclear. Here, we attempt to answer this question by generating winning tickets for one training configuration (optimizer and dataset) and evaluating their performance on another configuration. Perhaps surprisingly, we found that, within the natural images domain, winning ticket initializations generalized across a variety of datasets, including Fashion MNIST, SVHN, CIFAR-10/100, ImageNet, and Places365, often achieving performance close to that of winning tickets generated on the same dataset. Moreover, winning tickets generated using larger datasets consistently transferred better than those generated using smaller datasets. We also found that winning ticket initializations generalize across optimizers with high performance. These results suggest that winning ticket initializations generated by sufficiently large datasets contain inductive biases generic to neural networks more broadly which improve training across many settings and provide hope for the development of better initialization methods.

Keywords: Efficient Training Methods · Deep Learning · Algorithms -> Multitask and Transfer Learning; Deep Learning; Deep Learning -> CNN Architectures; Deep Learning · Efficient In
[ Paper]  [ Slides] 

Author Information

Ari Morcos (Facebook AI Research)
Haonan Yu (Facebook AI Research)
Michela Paganini (Facebook AI Research)
Yuandong Tian (Facebook AI Research)

More from the Same Authors