CoAtNet-DeepMoE: A Convolution-Attention Hybrid with DeepSeek Mixture-of-Experts for Parameter-Efficient Tomato Disease Classification

arXiv:2609.18038 · cs.CV · Submitted 2026-09-16 · Read on arXiv

cs.CV

Submitted: 2026-09-16

Updated: 2026-09-16

Code: https://github.com/nadimbrur/CoAt-MoE

Terminology

• Accuracy — 49× in this paper · explained in 1 episode
• Transformer — 15× in this paper · explained in 2 episodes
• Feature Extraction — 10× in this paper · explained in 2 episodes
• Attention Mechanism — 8× in this paper · explained in 3 episodes
• Robustness — 6× in this paper · explained in 5 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Transfer Learning — 5× in this paper · explained in 10 episodes
• Ablation study — 4× in this paper · explained in 1 episode
• DeepSeek Mixture-of-Experts — 4× in this paper
• Leaf Mold — 3× in this paper
• Mixture-of-Experts (MoE) — 3× in this paper · explained in 7 episodes
• Mosaic Virus — 3× in this paper
• Yellow Leaf Curl Virus — 3× in this paper
• Benchmark — 2× in this paper · explained in 2 episodes
• Data Augmentation — 2× in this paper · explained in 7 episodes
• Feature Selection — 2× in this paper · explained in 4 episodes
• Generalizability — 2× in this paper · explained in 6 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Global Average Pooling — 2× in this paper
• Learning Rate — 2× in this paper
• Ablation Studies — 1× in this paper · explained in 1 episode
• Activation Function — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• EfficientNetB0 with Squeeze-and-Excitation — 1× in this paper
• Few-Shot Learning — 1× in this paper · explained in 4 episodes
• Gray Level Co-occurrence Matrix — 1× in this paper
• Hybrid Models — 1× in this paper · explained in 2 episodes
• K-Nearest Neighbors — 1× in this paper
• Loss Function — 1× in this paper · explained in 2 episodes
• Mixture of Experts — 1× in this paper · explained in 2 episodes
• Parameter Efficiency — 1× in this paper · explained in 3 episodes
• Scale-Invariant Feature Transform — 1× in this paper

Sources

• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• An Introduction to Convolutional Neural Networks
• A Comprehensive Survey of Mixture-of-Experts: Algorithms, Theory, and Applications
• DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models