Boosted Training of Lightweight Early Exits for Optimizing CNN Image Classification Inference

• URL: http://arxiv.org/abs/2509.08318v1
• Date: Wed, 10 Sep 2025 06:47:49 GMT
• Title: Boosted Training of Lightweight Early Exits for Optimizing CNN Image Classification Inference
• Authors: Yehudit Aperstein, Alexander Apartsin,
• Abstract summary: We introduce a sequential training approach that aligns branch training with inference-time data distributions.<n>Experiments on the CINIC-10 dataset with a ResNet18 backbone demonstrate that BTS-EE consistently outperforms non-boosted training.<n>These results offer practical efficiency gains for applications such as industrial inspection, embedded vision, and UAV-based monitoring.
• Score: 47.027290803102666
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Real-time image classification on resource-constrained platforms demands inference methods that balance accuracy with strict latency and power budgets. Early-exit strategies address this need by attaching auxiliary classifiers to intermediate layers of convolutional neural networks (CNNs), allowing "easy" samples to terminate inference early. However, conventional training of early exits introduces a covariance shift: downstream branches are trained on full datasets, while at inference they process only the harder, non-exited samples. This mismatch limits efficiency--accuracy trade-offs in practice. We introduce the Boosted Training Scheme for Early Exits (BTS-EE), a sequential training approach that aligns branch training with inference-time data distributions. Each branch is trained and calibrated before the next, ensuring robustness under selective inference conditions. To further support embedded deployment, we propose a lightweight branch architecture based on 1D convolutions and a Class Precision Margin (CPM) calibration method that enables per-class threshold tuning for reliable exit decisions. Experiments on the CINIC-10 dataset with a ResNet18 backbone demonstrate that BTS-EE consistently outperforms non-boosted training across 64 configurations, achieving up to 45 percent reduction in computation with only 2 percent accuracy degradation. These results expand the design space for deploying CNNs in real-time image processing systems, offering practical efficiency gains for applications such as industrial inspection, embedded vision, and UAV-based monitoring.

Related papers

• Online Bayesian Imbalanced Learning with Bregman-Calibrated Deep Networks [0.7106986689736825]
  We present textitOnline Bayesian Imbalanced Learning (OBIL), a principled framework that decouples likelihood-ratio estimation from class-prior assumptions.<n>Our approach builds on the established connection between Bregman divergences and proper scoring rules to show that deep networks trained with such losses produce posterior probability estimates.<n>We prove that these likelihood-ratio estimates remain valid under arbitrary changes in class priors and cost structures, requiring only a threshold adjustment for optimal Bayes decisions.
  arXiv  Detail & Related papers  (2026-02-08T21:23:00Z)
• Winning the Pruning Gamble: A Unified Approach to Joint Sample and Token Pruning for Efficient Supervised Fine-Tuning [71.30276778807068]
  We propose a unified framework that strategically coordinates sample pruning and token pruning.<n>Q-Tuning achieves a +38% average improvement over the full-data SFT baseline using only 12.5% of the original training data.
  arXiv  Detail & Related papers  (2025-09-28T13:27:38Z)
• Confidence-gated training for efficient early-exit neural networks [49.78598138251519]
  Early-exit neural networks reduce inference cost by enabling confident predictions at intermediate layers.<n>We propose Confidence-Gated Training (CGT), a paradigm that conditionally propagates gradients from deeper exits only when preceding exits fail.
  arXiv  Detail & Related papers  (2025-09-22T15:18:21Z)
• Bridging Precision and Confidence: A Train-Time Loss for Calibrating Object Detection [58.789823426981044]
  We propose a novel auxiliary loss formulation that aims to align the class confidence of bounding boxes with the accurateness of predictions. Our results reveal that our train-time loss surpasses strong calibration baselines in reducing calibration error for both in and out-domain scenarios.
  arXiv  Detail & Related papers  (2023-03-25T08:56:21Z)
• Boosting Low-Data Instance Segmentation by Unsupervised Pre-training with Saliency Prompt [103.58323875748427]
  This work offers a novel unsupervised pre-training solution for low-data regimes. Inspired by the recent success of the Prompting technique, we introduce a new pre-training method that boosts QEIS models. Experimental results show that our method significantly boosts several QEIS models on three datasets.
  arXiv  Detail & Related papers  (2023-02-02T15:49:03Z)
• Anticipate, Ensemble and Prune: Improving Convolutional Neural Networks via Aggregated Early Exits [7.967995669387532]
  We present Anticipate, Ensemble and Prune (AEP), a new training technique based on weighted ensembles of early exits. AEP can yield average accuracy improvements of up to 15% over traditional training.
  arXiv  Detail & Related papers  (2023-01-28T11:45:11Z)
• DE-CROP: Data-efficient Certified Robustness for Pretrained Classifiers [21.741026088202126]
  We propose a novel way to certify the robustness of pretrained models using only a few training samples. Our proposed approach generates class-boundary and interpolated samples corresponding to each training sample. We obtain significant improvements over the baseline on multiple benchmark datasets and also report similar performance under the challenging black box setup.
  arXiv  Detail & Related papers  (2022-10-17T10:41:18Z)
• Learning from Data with Noisy Labels Using Temporal Self-Ensemble [11.245833546360386]
  Deep neural networks (DNNs) have an enormous capacity to memorize noisy labels. Current state-of-the-art methods present a co-training scheme that trains dual networks using samples associated with small losses. We propose a simple yet effective robust training scheme that operates by training only a single network.
  arXiv  Detail & Related papers  (2022-07-21T08:16:31Z)
• Efficient Augmentation for Imbalanced Deep Learning [8.38844520504124]
  We study a convolutional neural network's internal representation of imbalanced image data. We measure the generalization gap between a model's feature embeddings in the training and test sets, showing that the gap is wider for minority classes. This insight enables us to design an efficient three-phase CNN training framework for imbalanced data.
  arXiv  Detail & Related papers  (2022-07-13T09:43:17Z)
• Distributed Adversarial Training to Robustify Deep Neural Networks at Scale [100.19539096465101]
  Current deep neural networks (DNNs) are vulnerable to adversarial attacks, where adversarial perturbations to the inputs can change or manipulate classification. To defend against such attacks, an effective approach, known as adversarial training (AT), has been shown to mitigate robust training. We propose a large-batch adversarial training framework implemented over multiple machines.
  arXiv  Detail & Related papers  (2022-06-13T15:39:43Z)
• FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training [62.932299614630985]
  We propose FracTrain that integrates progressive fractional quantization which gradually increases the precision of activations, weights, and gradients.<n>FracTrain reduces computational cost and hardware-quantified energy/latency of DNN training while achieving a comparable or better (-0.12%+1.87%) accuracy.
  arXiv  Detail & Related papers  (2020-12-24T05:24:10Z)

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.