3 research outputs found
ZiCo-BC: A Bias Corrected Zero-Shot NAS for Vision Tasks
Zero-Shot Neural Architecture Search (NAS) approaches propose novel
training-free metrics called zero-shot proxies to substantially reduce the
search time compared to the traditional training-based NAS. Despite the success
on image classification, the effectiveness of zero-shot proxies is rarely
evaluated on complex vision tasks such as semantic segmentation and object
detection. Moreover, existing zero-shot proxies are shown to be biased towards
certain model characteristics which restricts their broad applicability. In
this paper, we empirically study the bias of state-of-the-art (SOTA) zero-shot
proxy ZiCo across multiple vision tasks and observe that ZiCo is biased towards
thinner and deeper networks, leading to sub-optimal architectures. To solve the
problem, we propose a novel bias correction on ZiCo, called ZiCo-BC. Our
extensive experiments across various vision tasks (image classification, object
detection and semantic segmentation) show that our approach can successfully
search for architectures with higher accuracy and significantly lower latency
on Samsung Galaxy S10 devices.Comment: Accepted at ICCV-Workshop on Resource-Efficient Deep Learning, 202
DONNAv2 -- Lightweight Neural Architecture Search for Vision tasks
With the growing demand for vision applications and deployment across edge
devices, the development of hardware-friendly architectures that maintain
performance during device deployment becomes crucial. Neural architecture
search (NAS) techniques explore various approaches to discover efficient
architectures for diverse learning tasks in a computationally efficient manner.
In this paper, we present the next-generation neural architecture design for
computationally efficient neural architecture distillation - DONNAv2 .
Conventional NAS algorithms rely on a computationally extensive stage where an
accuracy predictor is learned to estimate model performance within search
space. This building of accuracy predictors helps them predict the performance
of models that are not being finetuned. Here, we have developed an elegant
approach to eliminate building the accuracy predictor and extend DONNA to a
computationally efficient setting. The loss metric of individual blocks forming
the network serves as the surrogate performance measure for the sampled models
in the NAS search stage. To validate the performance of DONNAv2 we have
performed extensive experiments involving a range of diverse vision tasks
including classification, object detection, image denoising, super-resolution,
and panoptic perception network (YOLOP). The hardware-in-the-loop experiments
were carried out using the Samsung Galaxy S10 mobile platform. Notably, DONNAv2
reduces the computational cost of DONNA by 10x for the larger datasets.
Furthermore, to improve the quality of NAS search space, DONNAv2 leverages a
block knowledge distillation filter to remove blocks with high inference costs.Comment: Accepted at ICCV-Workshop on Resource-Efficient Deep Learning, 202