Partially-Connected Differentiable Architecture Search for Deepfake and Spoofing Detection

• URL: http://arxiv.org/abs/2104.03123v1
• Date: Wed, 7 Apr 2021 13:53:20 GMT
• Title: Partially-Connected Differentiable Architecture Search for Deepfake and Spoofing Detection
• Authors: Wanying Ge, Michele Panariello, Jose Patino, Massimiliano Todisco and Nicholas Evans
• Abstract summary: This paper reports the first successful application of a differentiable architecture search (DARTS) approach to the deepfake and spoofing detection problems. DARTS operates upon a continuous, differentiable search space which enables both the architecture and parameters to be optimised via gradient descent.
• Score: 14.792884010821762
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper reports the first successful application of a differentiable architecture search (DARTS) approach to the deepfake and spoofing detection problems. An example of neural architecture search, DARTS operates upon a continuous, differentiable search space which enables both the architecture and parameters to be optimised via gradient descent. Solutions based on partially-connected DARTS use random channel masking in the search space to reduce GPU time and automatically learn and optimise complex neural architectures composed of convolutional operations and residual blocks. Despite being learned quickly with little human effort, the resulting networks are competitive with the best performing systems reported in the literature. Some are also far less complex, containing 85% fewer parameters than a Res2Net competitor.

Related papers

• DASS: Differentiable Architecture Search for Sparse neural networks [0.5735035463793009]
  We find that the architectures designed for dense networks by differentiable architecture search methods are ineffective when pruning mechanisms are applied to them. In this paper, we propose a new method to search for sparsity-friendly neural architectures. We do this by adding two new sparse operations to the search space and modifying the search objective.
  arXiv  Detail & Related papers  (2022-07-14T14:53:50Z)
• D-DARTS: Distributed Differentiable Architecture Search [75.12821786565318]
  Differentiable ARchiTecture Search (DARTS) is one of the most trending Neural Architecture Search (NAS) methods. We propose D-DARTS, a novel solution that addresses this problem by nesting several neural networks at cell-level.
  arXiv  Detail & Related papers  (2021-08-20T09:07:01Z)
• iDARTS: Differentiable Architecture Search with Stochastic Implicit Gradients [75.41173109807735]
  Differentiable ARchiTecture Search (DARTS) has recently become the mainstream of neural architecture search (NAS) We tackle the hypergradient computation in DARTS based on the implicit function theorem. We show that the architecture optimisation with the proposed method, named iDARTS, is expected to converge to a stationary point.
  arXiv  Detail & Related papers  (2021-06-21T00:44:11Z)
• Combined Depth Space based Architecture Search For Person Re-identification [70.86236888223569]
  We aim to design a lightweight and suitable network for person re-identification (ReID) We propose a novel search space called Combined Depth Space (CDS), based on which we search for an efficient network architecture, which we call CDNet. We then propose a low-cost search strategy named the Top-k Sample Search strategy to make full use of the search space and avoid trapping in local optimal result.
  arXiv  Detail & Related papers  (2021-04-09T02:40:01Z)
• Towards Improving the Consistency, Efficiency, and Flexibility of Differentiable Neural Architecture Search [84.4140192638394]
  Most differentiable neural architecture search methods construct a super-net for search and derive a target-net as its sub-graph for evaluation. In this paper, we introduce EnTranNAS that is composed of Engine-cells and Transit-cells. Our method also spares much memory and computation cost, which speeds up the search process.
  arXiv  Detail & Related papers  (2021-01-27T12:16:47Z)
• ISTA-NAS: Efficient and Consistent Neural Architecture Search by Sparse Coding [86.40042104698792]
  We formulate neural architecture search as a sparse coding problem. In experiments, our two-stage method on CIFAR-10 requires only 0.05 GPU-day for search. Our one-stage method produces state-of-the-art performances on both CIFAR-10 and ImageNet at the cost of only evaluation time.
  arXiv  Detail & Related papers  (2020-10-13T04:34:24Z)
• Disentangled Neural Architecture Search [7.228790381070109]
  We propose disentangled neural architecture search (DNAS) which disentangles the hidden representation of the controller into semantically meaningful concepts. DNAS successfully disentangles the architecture representations, including operation selection, skip connections, and number of layers. Dense-sampling leads to neural architecture search with higher efficiency and better performance.
  arXiv  Detail & Related papers  (2020-09-24T03:35:41Z)
• VINNAS: Variational Inference-based Neural Network Architecture Search [2.685668802278155]
  We present a differentiable variational inference-based NAS method for searching sparse convolutional neural networks. Our method finds diverse network cells, while showing state-of-the-art accuracy with up to almost 2 times fewer non-zero parameters.
  arXiv  Detail & Related papers  (2020-07-12T21:47:35Z)
• AlphaGAN: Fully Differentiable Architecture Search for Generative Adversarial Networks [15.740179244963116]
  Generative Adversarial Networks (GANs) are formulated as minimax game problems, whereby generators attempt to approach real data distributions by virtue of adversarial learning against discriminators. In this work, we aim to boost model learning from the perspective of network architectures, by incorporating recent progress on automated architecture search into GANs. We propose a fully differentiable search framework for generative adversarial networks, dubbed alphaGAN.
  arXiv  Detail & Related papers  (2020-06-16T13:27:30Z)
• RC-DARTS: Resource Constrained Differentiable Architecture Search [162.7199952019152]
  We propose the resource constrained differentiable architecture search (RC-DARTS) method to learn architectures that are significantly smaller and faster. We show that the RC-DARTS method learns lightweight neural architectures which have smaller model size and lower computational complexity.
  arXiv  Detail & Related papers  (2019-12-30T05:02:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.