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alvinwan/shiftresnet|cifar


alvinwan/shiftresnet-cifar: ResNet with Shift, Depthwise, or ... - GitHub

ResNet with Shift, Depthwise, or Convolutional Operations for CIFAR-100, CIFAR-10 on PyTorch - alvinwan/shiftresnet-cifar.

shiftresnet-cifar/count.py at master - GitHub

ResNet with Shift, Depthwise, or Convolutional Operations for CIFAR-100, CIFAR-10 on PyTorch - shiftresnet-cifar/count.py at master · alvinwan/shiftresnet-

Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions

... Mark official. alvinwan/shiftresnet-cifar. 140. DeadAt0m/ActiveSparseShifts-PyTorch. 34. alvinwan/shiftresnet-cifar. 140. DeadAt0m/ActiveSparseShifts-PyTorch.

SHIFT OPERATION ANALYSIS USING CIFAR10 AND CIFAR100

Alvin Wan; Xiangyu Yue; [...] Kurt Keutzer. Neural networks rely on convolutions to aggregate spatial information. However ...

Neural-Backed Decision Trees - NBDT - Alvin Wan

Neural-Backed Decision Trees. Alvin Wan ... improved accuracy: NBDTs match or outperform modern neural networks on CIFAR, ImageNet and better generalize to unseen ...

Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions

To demonstrate the operation's efficacy, we replace ResNet's 3x3 convolutions with shift-based modules for improved CIFAR10 and CIFAR100 ...

SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural ...

... /squeezeDet official. 739. alvinwan/shiftresnet-cifar. 140. omni-us/squeezedet-keras. 129. Yang-YiFan/DiracDeltaNet. 31. supernlogn/squeezeDetTL.

Profile of alvinwan - PyPI

Alvin Wan. Username alvinwan ... Making decision trees competitive with state-of-the-art neural networks on CIFAR10, CIFAR100, TinyImagenet200, Imagenet.

Alvin Wan - Apple - LinkedIn

... CIFAR10 and CIFAR100 accuracy using 60% fewer parameters; we additionally demonstrate the operation's resilience to parameter reduction on ImageNet ...

[2004.00221] NBDT: Neural-Backed Decision Trees - arXiv

... CIFAR, ImageNet and better generalize to unseen classes by up to 16%. ... Submission history. From: Alvin Wan [view email] [v1] Wed, 1 Apr ...

Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions

Bichen Wu, Alvin Wan, Xiangyu ... To demonstrate the operation's efficacy, we replace ResNet's 3x3 convolutions with shift-based modules for improved CIFAR ...

Efficiently Designing Efficient Deep Neural Networks by Alvin Wan

Hybrid Models. Recent work produces hybrid decision tree and neural network models to scale up to datasets like CIFAR10 [56], CIFAR100 [56], TinyImageNet [ ...

How to write a paper - Alvin Wan

improved accuracy: NBDTs match or outperform modern neural networks on CIFAR, ImageNet and better generalize to unseen classes byup to 16%. Furthermore, our ...

Context-Aware Fuzzing for Robustness Enhancement of Deep ...

Shiftresnet-cifar. https://github.com/alvinwan/shiftresnet-cifar. Google Scholar. [3]. 2021. Adapt. https://github.com/kupl/ADAPT. Google ...

Alvin WAN | University of California, Berkeley - ResearchGate

Alvin Wan ... Figure 3: Illustration of the Conv-Shift-Conv CSC module and the. Figure 4: This figure shows that ShiftResNet family members are...

Efficiently Designing Efficient Deep Neural Networks

github.com/alvinwan/neural-backed-decision-trees.1. 1The contents ... the CIFAR-trained model may predict any CIFAR Animal class). See ...

NBDT: Neural-Backed Decision Tree - OpenReview

Code and pretrained NBDTs are at https://github.com/alvinwan/neural-backed-decision-trees. One-sentence Summary: Neural-Backed Decision Trees ...

load_pretrained_nbdts.ipynb - Colab

dataset='CIFAR10', arch='wrn28_10_cifar10', model=model). [ ] ... cls = DATASET_TO_CLASSES['CIFAR10'][predicted[0]] print(cls). Colab paid ...

edumunozsala/vit_base-224-in21k-ft-cifar10 - Hugging Face

... Alvin Wan and Peizhao Zhang and Zhicheng Yan and Masayoshi Tomizuka and Joseph Gonzalez and Kurt Keutzer and Peter Vajda}, year={2020 ...

edumunozsala/vit_base-224-in21k-ft-cifar100 - Hugging Face

... Alvin Wan and ... Dataset. Link to dataset description. The CIFAR-10 and CIFAR-100 are labeled subsets of the 80 million tiny images dataset.