Learning by Coding
Recognition tasks, such as object recognition and keypoint estimation, have seen widespread adoption in recent years. Most state-of-the-art methods for these tasks use deep networks that are computationally expensive and have huge memory footprints. This makes it exceedingly difficult to deploy these systems on low power embedded devices. Hence, the importance of decreasing the storage requirements and the amount of computation in such models is paramount. The recently proposed Lottery Ticket Hypothesis (LT
In the graph below, we show the performance of directly pruning the entire network for various tasks, across backbones and sparsities. We can see that pruned version either performs better or maintains a comparable accuracy until 80% sparsity.
Learning by Coding