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arxiv: 2606.30516 · v1 · pith:FRSP3A5Znew · submitted 2026-06-29 · 💻 cs.CV

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

Lukas Meiner , Jens Mehnert , Alexandru Paul Condurache This is my paper

Pith reviewed 2026-06-30 06:24 UTC · model grok-4.3

classification 💻 cs.CV
keywords CNN compressiontraining-free inferencedynamic compressionlocality-sensitive hashingchannel mergingResNetCIFAR-10ImageNet
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The pith

HASTE uses locality-sensitive hashing to merge redundant channels patch-wise in pre-trained CNNs at inference, enabling dynamic compression without any retraining or data access.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces HASTE as a plug-and-play module that inserts into existing convolutional layers of pre-trained networks. At runtime it applies locality-sensitive hashing to feature-map patches to detect and merge similar channels, which shrinks both the input depth and the matching filter weights for cheaper matrix multiplications. Experiments report that this yields up to 46.2 percent fewer FLOPs on ResNet-34 with CIFAR-10 while losing only 1.25 percent accuracy, and the same pattern holds across several architectures on ImageNet. A reader would care because the method removes the usual requirements for fine-tuning or even access to the original training set, making large models runnable on constrained hardware without extra training cost.

Core claim

HASTE is a plug-and-play convolution module that at inference time uses locality-sensitive hashing to identify and merge redundant channels of latent feature maps on a patch-wise basis; this simultaneously compresses the depth of both input features and their corresponding filters, resulting in computationally cheaper convolutions that require no retraining.

What carries the argument

HASTE module performing locality-sensitive hashing for patch-wise redundant channel merging that shrinks both feature-map depth and filter depth inside standard convolutions.

If this is right

  • Pre-trained CNNs can be deployed on resource-limited devices by swapping in HASTE modules at inference without any additional training step.
  • Compression level becomes adjustable at runtime by changing the hashing threshold or patch size.
  • The same channel-merging idea applies across multiple CNN families including ResNet and others tested on both CIFAR-10 and ImageNet.
  • The approach avoids any need for the original training data during the compression step.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The patch-wise channel merging may generalize to other layer types such as depth-wise convolutions or attention blocks if similar redundancy patterns exist.
  • Runtime steering could be driven by external signals like battery level or latency targets without retraining the underlying model.
  • Because the paper links the scheme to token merging in transformers, a unified merging framework across CNNs and ViTs becomes a natural next direction to test.

Load-bearing premise

Locality-sensitive hashing can reliably detect redundant channels on patches so that merging them keeps the network accurate enough without any fine-tuning.

What would settle it

A controlled run on ResNet-34 with CIFAR-10 in which HASTE produces either more than a 3 percent accuracy drop or less than 20 percent FLOPs reduction while still using the same pre-trained weights.

read the original 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 specialized training or fine-tuning, limiting their effectiveness when applied to pre-trained models and requiring data access. To address this gap, we propose HASTE (Hashing for Tractable Efficiency), a plug-and-play convolution module that enables training-free, dynamic compression of large pre-trained CNNs. At inference time, HASTE uses locality-sensitive hashing to identify and merge redundant channels of latent feature maps on a patch-wise basis. This process simultaneously compresses the depth of both input features and their corresponding filters, resulting in computationally cheaper convolutions. We conduct extensive experiments on CIFAR-10 and ImageNet across a range of architectures, demonstrating a 46.2% FLOPs reduction in a ResNet34 on CIFAR-10 with only a 1.25% drop in accuracy, without any retraining. We support our claims by comprehensive ablation studies to validate our core design choices, an analysis of the method's properties and limitations, and a discussion that connects our channel merging scheme to the conceptually related task of token merging in Vision Transformers. Our results demonstrate that HASTE provides an effective solution for steerable compression of pre-trained CNNs at runtime, opening new possibilities for the deployment of efficient deep learning methods.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 3 minor

Summary. The paper proposes HASTE, a plug-and-play convolution module that enables training-free, dynamic, and steerable compression of pre-trained CNNs. At inference, locality-sensitive hashing identifies and merges redundant channels in latent feature maps on a patch-wise basis, simultaneously reducing the depth of input features and corresponding filters to yield cheaper convolutions. Experiments across CIFAR-10 and ImageNet on multiple architectures report e.g. a 46.2% FLOPs reduction for ResNet34 on CIFAR-10 with a 1.25% accuracy drop and no retraining; the work includes ablation studies validating design choices, analysis of method properties and limitations, and a discussion relating the channel-merging scheme to token merging in Vision Transformers.

Significance. If the empirical results hold under scrutiny, the contribution is significant because it closes a practical gap: existing dynamic CNN execution methods typically require specialized training, fine-tuning, or data access, whereas HASTE operates on frozen pre-trained models with no data or gradient updates. The steerable and dynamic nature at runtime, combined with the explicit connection to token merging, adds conceptual value. Credit is given for the extensive benchmark coverage, ablation studies, and the training-free claim being directly supported by the reported measurements rather than fitted parameters.

minor comments (3)
  1. [Abstract] Abstract: the phrase 'comprehensive ablation studies' would benefit from a brief enumeration of the specific design choices ablated (e.g., hash-function count, patch size, merging threshold) to allow readers to assess coverage immediately.
  2. The description of the LSH-based merging procedure would be clearer if the precise definition of a 'patch' (spatial support and stride) and the exact criterion for declaring two channels redundant were stated in a single equation or pseudocode block early in the methods.
  3. Figure captions and axis labels should explicitly state whether reported accuracy/FLOPs numbers are means over multiple random seeds or single runs; this is a minor but recurring clarity issue for reproducibility.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive review, accurate summary of our contributions, and recommendation of minor revision. The significance assessment aligns with our claims regarding the training-free operation on frozen models.

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper introduces HASTE as an empirical plug-and-play module relying on locality-sensitive hashing for patch-wise channel merging in pre-trained CNNs. All central claims (FLOPs reduction, accuracy preservation) are supported by experimental results on CIFAR-10 and ImageNet, ablations, and architecture-specific measurements rather than any derivation, equation, or parameter fit that reduces to its own inputs by construction. No self-citation chains, uniqueness theorems, or ansatzes are invoked as load-bearing premises. The method is self-contained against external benchmarks via reported empirical outcomes.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 1 invented entities

The central claim depends on the empirical effectiveness of LSH for identifying mergeable channels; no explicit free parameters or invented physical entities are stated in the abstract.

free parameters (1)
  • LSH parameters (number of hash functions, bucket thresholds)
    Control similarity detection and merging decisions; values not reported in abstract.
axioms (1)
  • domain assumption Locality-sensitive hashing preserves enough similarity structure in CNN feature channels to allow safe merging
    Invoked by the core merging step described in the abstract.
invented entities (1)
  • HASTE convolution module no independent evidence
    purpose: Plug-and-play component that performs dynamic channel merging
    New module introduced by the paper; no independent evidence outside the reported experiments.

pith-pipeline@v0.9.1-grok · 5797 in / 1310 out tokens · 38166 ms · 2026-06-30T06:24:54.973335+00:00 · methodology

discussion (0)

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