Emergence of Latent Binary Encoding in Deep Neural Network Classifiers

• URL: http://arxiv.org/abs/2310.08224v4
• Date: Tue, 28 May 2024 06:55:23 GMT
• Title: Emergence of Latent Binary Encoding in Deep Neural Network Classifiers
• Authors: Luigi Sbailò, Luca Ghiringhelli,
• Abstract summary: We investigate the emergence of binary encoding within the latent space of deep-neural-network classifiers. By analyzing several datasets of increasing complexity, we provide empirical evidence that the emergence of binary encoding dramatically enhances robustness.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We investigate the emergence of binary encoding within the latent space of deep-neural-network classifiers. Such binary encoding is induced by the introduction of a linear penultimate layer, which employs during training a loss function specifically designed to compress the latent representations. As a result of a trade-off between compression and information retention, the network learns to assume only one of two possible values for each dimension in the latent space. The binary encoding is provoked by the collapse of all representations of the same class to the same point, which corresponds to the vertex of a hypercube. By analyzing several datasets of increasing complexity, we provide empirical evidence that the emergence of binary encoding dramatically enhances robustness while also significantly improving the reliability and generalization of the network.

Related papers

• Robust Stochastically-Descending Unrolled Networks [85.6993263983062]
  Deep unrolling is an emerging learning-to-optimize method that unrolls a truncated iterative algorithm in the layers of a trainable neural network. We show that convergence guarantees and generalizability of the unrolled networks are still open theoretical problems. We numerically assess unrolled architectures trained under the proposed constraints in two different applications.
  arXiv  Detail & Related papers  (2023-12-25T18:51:23Z)
• On the Dynamics Under the Unhinged Loss and Beyond [104.49565602940699]
  We introduce the unhinged loss, a concise loss function, that offers more mathematical opportunities to analyze closed-form dynamics. The unhinged loss allows for considering more practical techniques, such as time-vary learning rates and feature normalization.
  arXiv  Detail & Related papers  (2023-12-13T02:11:07Z)
• Convergence and Implicit Regularization Properties of Gradient Descent for Deep Residual Networks [7.090165638014331]
  We prove linear convergence of gradient descent to a global minimum for the training of deep residual networks with constant layer width and smooth activation function. We show that the trained weights, as a function of the layer index, admits a scaling limit which is H"older continuous as the depth of the network tends to infinity.
  arXiv  Detail & Related papers  (2022-04-14T22:50:28Z)
• An Intermediate-level Attack Framework on The Basis of Linear Regression [89.85593878754571]
  This paper substantially extends our work published at ECCV, in which an intermediate-level attack was proposed to improve the transferability of some baseline adversarial examples. We advocate to establish a direct linear mapping from the intermediate-level discrepancies (between adversarial features and benign features) to classification prediction loss of the adversarial example. We show that 1) a variety of linear regression models can all be considered in order to establish the mapping, 2) the magnitude of the finally obtained intermediate-level discrepancy is linearly correlated with adversarial transferability, and 3) further boost of the performance can be achieved by performing multiple runs of the baseline attack with
  arXiv  Detail & Related papers  (2022-03-21T03:54:53Z)
• Discriminability-enforcing loss to improve representation learning [20.4701676109641]
  We introduce a new loss term inspired by the Gini impurity to minimize the entropy of individual high-level features. Although our Gini loss induces highly-discriminative features, it does not ensure that the distribution of the high-level features matches the distribution of the classes. Our empirical results show that integrating our novel loss terms into the training objective consistently outperforms the models trained with cross-entropy alone.
  arXiv  Detail & Related papers  (2022-02-14T22:31:37Z)
• Defensive Tensorization [113.96183766922393]
  We propose tensor defensiveization, an adversarial defence technique that leverages a latent high-order factorization of the network. We empirically demonstrate the effectiveness of our approach on standard image classification benchmarks. We validate the versatility of our approach across domains and low-precision architectures by considering an audio task and binary networks.
  arXiv  Detail & Related papers  (2021-10-26T17:00:16Z)
• An Unconstrained Layer-Peeled Perspective on Neural Collapse [20.75423143311858]
  We introduce a surrogate model called the unconstrained layer-peeled model (ULPM) We prove that gradient flow on this model converges to critical points of a minimum-norm separation problem exhibiting neural collapse in its global minimizer. We show that our results also hold during the training of neural networks in real-world tasks when explicit regularization or weight decay is not used.
  arXiv  Detail & Related papers  (2021-10-06T14:18:47Z)
• The Interplay Between Implicit Bias and Benign Overfitting in Two-Layer Linear Networks [51.1848572349154]
  neural network models that perfectly fit noisy data can generalize well to unseen test data. We consider interpolating two-layer linear neural networks trained with gradient flow on the squared loss and derive bounds on the excess risk.
  arXiv  Detail & Related papers  (2021-08-25T22:01:01Z)
• Eccentric Regularization: Minimizing Hyperspherical Energy without explicit projection [0.913755431537592]
  We introduce a novel regularizing loss function which simulates a pairwise repulsive force between items. We show that minimizing this loss function in isolation achieves a hyperspherical distribution. We apply this method of Eccentric Regularization to an autoencoder, and demonstrate its effectiveness in image generation, representation learning and downstream classification tasks.
  arXiv  Detail & Related papers  (2021-04-23T13:55:17Z)
• Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning [97.28695683236981]
  More gradient updates decrease the expressivity of the current value network. We demonstrate this phenomenon on Atari and Gym benchmarks, in both offline and online RL settings.
  arXiv  Detail & Related papers  (2020-10-27T17:55:16Z)
• Implicit Bias of Gradient Descent for Wide Two-layer Neural Networks Trained with the Logistic Loss [0.0]
  Neural networks trained to minimize the logistic (a.k.a. cross-entropy) loss with gradient-based methods are observed to perform well in many supervised classification tasks. We analyze the training and generalization behavior of infinitely wide two-layer neural networks with homogeneous activations.
  arXiv  Detail & Related papers  (2020-02-11T15:42:09Z)

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.