Efficient Verification-Based Face Identification
- URL: http://arxiv.org/abs/2312.13240v2
- Date: Sat, 25 May 2024 17:57:41 GMT
- Title: Efficient Verification-Based Face Identification
- Authors: Amit Rozner, Barak Battash, Ofir Lindenbaum, Lior Wolf,
- Abstract summary: We study the problem of performing face verification with an efficient neural model $f$.
Our model leads to a substantially small $f$ requiring only 23k parameters and 5M floating point operations (FLOPS)
We use six face verification datasets to demonstrate that our method is on par or better than state-of-the-art models.
- Score: 50.616875565173274
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We study the problem of performing face verification with an efficient neural model $f$. The efficiency of $f$ stems from simplifying the face verification problem from an embedding nearest neighbor search into a binary problem; each user has its own neural network $f$. To allow information sharing between different individuals in the training set, we do not train $f$ directly but instead generate the model weights using a hypernetwork $h$. This leads to the generation of a compact personalized model for face identification that can be deployed on edge devices. Key to the method's success is a novel way of generating hard negatives and carefully scheduling the training objectives. Our model leads to a substantially small $f$ requiring only 23k parameters and 5M floating point operations (FLOPS). We use six face verification datasets to demonstrate that our method is on par or better than state-of-the-art models, with a significantly reduced number of parameters and computational burden. Furthermore, we perform an extensive ablation study to demonstrate the importance of each element in our method.
Related papers
- Just How Flexible are Neural Networks in Practice? [89.80474583606242]
It is widely believed that a neural network can fit a training set containing at least as many samples as it has parameters.
In practice, however, we only find solutions via our training procedure, including the gradient and regularizers, limiting flexibility.
arXiv Detail & Related papers (2024-06-17T12:24:45Z) - Federated Face Forgery Detection Learning with Personalized Representation [63.90408023506508]
Deep generator technology can produce high-quality fake videos that are indistinguishable, posing a serious social threat.
Traditional forgery detection methods directly centralized training on data.
The paper proposes a novel federated face forgery detection learning with personalized representation.
arXiv Detail & Related papers (2024-06-17T02:20:30Z) - Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints [59.39280540478479]
We propose sparse upcycling -- a simple way to reuse sunk training costs by initializing a sparsely activated Mixture-of-Experts model from a dense checkpoint.
We show that sparsely upcycled T5 Base, Large, and XL language models and Vision Transformer Base and Large models, respectively, significantly outperform their dense counterparts on SuperGLUE and ImageNet.
arXiv Detail & Related papers (2022-12-09T18:57:37Z) - Robust Few-shot Learning Without Using any Adversarial Samples [19.34427461937382]
A few efforts have been made to combine the few-shot problem with the robustness objective using sophisticated Meta-Learning techniques.
We propose a simple but effective alternative that does not require any adversarial samples.
Inspired by the cognitive decision-making process in humans, we enforce high-level feature matching between the base class data and their corresponding low-frequency samples.
arXiv Detail & Related papers (2022-11-03T05:58:26Z) - Training Neural Networks with Fixed Sparse Masks [19.58969772430058]
Recent work has shown that it is possible to update only a small subset of the model's parameters during training.
We show that it is possible to induce a fixed sparse mask on the model's parameters that selects a subset to update over many iterations.
arXiv Detail & Related papers (2021-11-18T18:06:01Z) - Deep learning for inverse problems with unknown operator [0.0]
In inverse problems where the forward operator $T$ is unknown, we have access to training data consisting of functions $f_i$ and their noisy images $Tf_i$.
We propose a new method that requires minimal assumptions on the data, and prove reconstruction rates that depend on the number of training points and the noise level.
arXiv Detail & Related papers (2021-08-05T17:21:12Z) - ${\rm N{\small ode}S{\small ig}}$: Random Walk Diffusion meets Hashing
for Scalable Graph Embeddings [7.025709586759654]
$rm Nsmall odeSsmall ig$ is a scalable embedding model that computes binary node representations.
$rm Nsmall odeSsmall ig$ exploits random walk diffusion probabilities via stable random projection hashing.
arXiv Detail & Related papers (2020-10-01T09:07:37Z) - Pre-Trained Models for Heterogeneous Information Networks [57.78194356302626]
We propose a self-supervised pre-training and fine-tuning framework, PF-HIN, to capture the features of a heterogeneous information network.
PF-HIN consistently and significantly outperforms state-of-the-art alternatives on each of these tasks, on four datasets.
arXiv Detail & Related papers (2020-07-07T03:36:28Z) - The Right Tool for the Job: Matching Model and Instance Complexities [62.95183777679024]
As NLP models become larger, executing a trained model requires significant computational resources incurring monetary and environmental costs.
We propose a modification to contextual representation fine-tuning which, during inference, allows for an early (and fast) "exit"
We test our proposed modification on five different datasets in two tasks: three text classification datasets and two natural language inference benchmarks.
arXiv Detail & Related papers (2020-04-16T04:28:08Z) - Towards Deep Learning Models Resistant to Large Perturbations [0.0]
Adversarial robustness has proven to be a required property of machine learning algorithms.
We show that the well-established algorithm called "adversarial training" fails to train a deep neural network given a large, but reasonable, perturbation magnitude.
arXiv Detail & Related papers (2020-03-30T12:03:09Z)
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.