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HASTE framework enables training-free compression of CNNs

By PulseAugur Editorial · [4 sources] ·

Researchers have developed HASTE, a novel framework designed to compress large pre-trained convolutional neural networks (CNNs) without requiring additional training or data access. This plug-and-play module utilizes locality-sensitive hashing to dynamically merge redundant channels during inference, thereby reducing computational costs. Experiments on datasets like CIFAR-10 and ImageNet show significant reductions in FLOPs, such as a 46.2% decrease in ResNet34 with a minimal accuracy drop. AI

IMPACT Enables more efficient deployment of large CNNs on resource-constrained devices without retraining.

RANK_REASON The cluster describes a new research paper detailing a novel framework for CNN compression.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 4 sources. How we write summaries →

HASTE framework enables training-free compression of CNNs

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The cluster describes a new research paper detailing a novel framework for CNN compression.
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COVERAGE [4]

  1. arXiv cs.LG TIER_1 English(EN) · David Gonz\'alez-Mart\'inez ·

    BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression

    arXiv:2509.25136v3 Announce Type: replace Abstract: Activation-aware low-rank factorization techniques yield strong compression results but are generally confined to linear layers, while existing whitening-based theory typically makes an implicit full-rank assumption on activatio…

  2. arXiv cs.AI TIER_1 English(EN) · Miko{\l}aj Janusz, Tomasz Wojnar, Yawei Li, Luca Benini, Kamil Adamczewski ·

    One Shot vs. Iterative: Rethinking Pruning Strategies for Model Compression

    arXiv:2508.13836v2 Announce Type: replace-cross Abstract: Pruning is a core technique for compressing neural networks to improve computational efficiency. This process is typically approached in two ways: one-shot pruning, which involves a single pass of training and pruning, and…

  3. arXiv cs.CV TIER_1 English(EN) · Lukas Meiner, Jens Mehnert, Alexandru Paul Condurache ·

    HASTE: A Framework for Training-Free, Dynamic, and Steerable Compression of Pre-Trained Convolutional Neural Networks

    arXiv:2606.30516v1 Announce Type: new Abstract: Deploying large convolutional neural networks (CNNs) on resource-constrained devices is challenging due to their high computational cost. While dynamic execution methods are promising, existing approaches for CNNs typically require …

  4. arXiv cs.CV TIER_1 English(EN) · Alexandru Paul Condurache ·

    HASTE: A Framework for Training-Free, Dynamic, and Steerable Compression of Pre-Trained Convolutional Neural Networks

    Deploying large convolutional neural networks (CNNs) on resource-constrained devices is challenging due to their high computational cost. While dynamic execution methods are promising, existing approaches for CNNs typically require specialized training or fine-tuning, limiting th…

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