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ARXIV:2603.08100 · VISION TRANSFORMERS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.08100VISION TRANSFORMERSSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
Adaptively prune large vision transformers' MLP layers to reduce parameters and FLOPs by 40% without significant performance loss, offering a more efficient model deployment.
Opportunity summary
Pain Adaptively prune large vision transformers' MLP layers to reduce parameters and FLOPs by 40% without significant performance loss, offering a more efficient model deployment.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
Adaptively prune large vision transformers' MLP layers to reduce parameters and FLOPs by 40% without significant performance loss, offering a more efficient model deployment. Nevertheless, their cumbersome parameters results in exorbitant computational and memory…
Large vision transformers present impressive scalability, as their performance can be well improved with increased model capacity. Nevertheless, their cumbersome parameters results in exorbitant computational and memory demands.
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Nevertheless, their cumbersome parameters results in exorbitant computational and memory demands.
Vision Transformers moved forward this cycle; last verified April 2026. Public score 7.0/10.
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Adaptively prune large vision transformers' MLP layers to reduce parameters and FLOPs by 40% without significant performance loss, offering a more efficient model deployment.
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10.48550/arXiv.2603.08100Adaptively prune large vision transformers' MLP layers to reduce parameters and FLOPs by 40% without significant performance loss, offering a more efficient model deployment.
Abstract
Large vision transformers present impressive scalability, as their performance can be well improved with increased model capacity. Nevertheless, their cumbersome parameters results in exorbitant computational and memory demands. By analyzing prevalent transformer structures, we find that multilayer perceptron (MLP) modules constitute the largest share of the model's parameters. In this paper, we propose an Adaptive MLP Pruning (AMP) method to substantially reduce the parameters of large vision transformers without obvious performance degradation. First, we adopt Taylor based method to evaluate neuron importance of MLP. However, the importance computation using one-hot cross entropy loss ignores the potential predictions on other categories, thus degrading the quality of the evaluated importance scores. To address this issue, we introduce label-free information entropy criterion to fully model the predictions of the original model for more accurate importance evaluation. Second, we rank the hidden neurons of MLP by the above importance scores and apply binary search algorithm to adaptively prune the ranked neurons according to the redundancy of different MLP modules, thereby avoiding the predefined compression ratio. Experimental results on several state-of-the-art large vision transformers, including CLIP and DINOv2, demonstrate that our method achieves roughly 40\% parameter and FLOPs reduction in a near lossless manner. Moreover, when the models are not finetuned after pruning, our method outperforms other pruning methods by significantly large margin. The source code and trained weights are available at https://github.com/visresearch/AMP.
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PROBLEM
Adaptively prune large vision transformers' MLP layers to reduce parameters and FLOPs by 40% without significant performance loss, offering a more efficient model deployment. Nevertheless, their cumbersome parameters results in exorbitant computational and memory demands.
METHOD
Large vision transformers present impressive scalability, as their performance can be well improved with increased model capacity. Nevertheless, their cumbersome parameters results in exorbitant computational and memory demands.
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Nevertheless, their cumbersome parameters results in exorbitant computational and memory demands.
WHY NOW
Vision Transformers moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Adaptively prune large vision transformers' MLP layers to reduce parameters and FLOPs by 40% without significant performance loss, offering a more efficient model deployment. Nevertheless, their cumbersome parameters results in exorbitant computational and memory demands.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Large vision transformers present impressive scalability, as their performance can be well improved with increased model capacity. Nevertheless, their cumbersome parameters results in exorbitant computational and memory demands.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Nevertheless, their cumbersome parameters results in exorbitant computational and memory demands.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Vision Transformers moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Adaptively prune large vision transformers' MLP layers to reduce parameters and FLOPs by 40% without significant performance loss, offering a more efficient model deployment.
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