One-Shot Neural Architecture Search: Maximising Diversity to Overcome Catastrophic Forgetting

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A Comprehensive Survey of Neural Architecture Search (2020, background): “NAS aims to design a neural architecture that achieves the best possible performance using limited computing resources in an automated way with minimal human intervention [26, 114].”
iDARTS: Differentiable Architecture Search with Stochastic Implicit Gradients (2021, background): “…more current works observe that DARTS is somewhat unreliable (Zela et al., 2020a; Chen & Hsieh, 2020; Li & Talwalkar, 2019; Sciuto et al., 2019; Zhang et al., 2020c; Li et al., 2021a; Zhang et al., 2020b) since it does not consistently yield excellent solutions, performing even worse than…”
Dynamic Slimmable Network (2021, unknown): “…efﬁciency of neural networks, including network pruning [43, 50, 21, 22, 51, 53], weight quantization [33], knowledge distillation [2, 56, 23], manually and automatically designing of efﬁcient networks [60, 57, 77, 55, 3, 75, 10, 18, 41, 76] and dynamic inference [4, 30, 64, 63, 44, 29, 14].”