具体来说,通过引入MoE机制,Sparse-MLP(MoE)网络在Top-1精度上超过了MLP-Mixer等多个基线模型。 应用前景与实践建议 Sparse-MLP(MoE)网络在图像分类任务中展现出了巨大的潜力,其动态选择特性和稀疏化操作使得模型在保持高性能的同时降低了计算复杂度。这一特性使得Sparse-MLP(MoE)网络在边缘计算、移动设备等资源受限...
MoE(x)=∑i=1NG(x)iEi(x)MoE(x)=∑i=1NG(x)iEi(x) 其中,$G(x)$是门控网络,用于计算以输入为条件的路由权重;$E_i(x)$是第$i$个专家层。 应用与优势 Sparse-MLP在图像识别任务中展现了其优势。在ImageNet-1k数据集上进行预训练时,Sparse-MLP模型在Top-1准确率上比密集MLP模型高出...
二者搭配,用于平衡二者中间包裹的 MOEs 的运算(降低 MOEs 运算时的通道数量并增加空间 patch 数量。 实验结果 We find that scaling MLP models in parameters and training them from scratch with limited training data will lead to an overfitting problem. Such finding is consistent with previous work on M...
Sparse Mixture of Experts (MoE) models are gaining traction due to their ability to enhance accuracy without proportionally increasing computational demands. Traditionally, significant computational resources have been invested in training dense Large Language Models (LLMs) with a single MLP layer...
Mixture-of-Experts (MoE) with sparse conditional computation has been proved an effective architecture for scaling attention-based models to more parameters with comparable computation cost. In this paper, we propose Sparse-MLP, scaling the recent MLP-Mixer model with sparse MoE layers, to achieve ...
本文提出轻量化多模态大模型 LLaVA-MoD,通过集成稀疏的专家混合(MoE)架构,优化小模型的网络结构,并提出 Dense-to-Sparse 蒸馏框架,结合两阶段蒸馏策略(模仿蒸馏+偏好蒸馏),实现全面的知识迁移。 该方案仅用 0.3% 数据和 23% 激活参数,即实现 2B 小模型综合性能超越 7B 大模型 8.8%,并在幻觉检测任务反超教师...
g t c ∈ [ 0 , 1 ] g_{t}^c \in [0,1]gtc∈[0,1]是对应策略c cc的门控得分,通过 MLP 和 sigmoid 激活从输入特征中获得。 令N t N_tNt表示重新映射的键/值总数: N t = ∑ c ∈ C size [ K ~ t c ] . N_t = \sum_{c \in C} \text{size}[\tilde{K}_{t}^c...
Their model, V-MoE, was applied to image classification and was able to use just half the amount of inference compute while matching the performance of prior state-of-the-art architectures. Lou et al. (2021) introduces a sparse MoE MLP model for image classification based on the MLP-Mixer...
This will allow you to modify scattermoe in this directory. pip install -e .Usagefrom scattermoe.mlp import MLP # Initialise module... mlp = MLP( input_size=x_dim, hidden_size=h_dim, activation=nn.GELU(), num_experts=E, top_k=k ) # Calling module... Y = mlp( X, # input ...
srun --gres=gpu:8 python CLIP-MoE/train/train_mcl.py --epochs 1 --exp-name clip-mcl-s1 --MCL-label-path CLIP-MoE/train/save_mcl_tmp/clip-mcl_0_pseudo_labels.pt --lock-except-mlp Then do the inference and clustering accordingly, and continue for N-1 stages....