Research Paper#Deep Learning, Transformers, Backpropagation, Pedestrian Detection🔬 ResearchAnalyzed: Jan 3, 2026 16:08
Backpropagation in Transformers for Pedestrian Detection
Published:Dec 29, 2025 09:26
•1 min read
•ArXiv
Analysis
This paper provides a detailed, manual derivation of backpropagation for transformer-based architectures, specifically focusing on layers relevant to next-token prediction and including LoRA layers for parameter-efficient fine-tuning. The authors emphasize the importance of understanding the backward pass for a deeper intuition of how each operation affects the final output, which is crucial for debugging and optimization. The paper's focus on pedestrian detection, while not explicitly stated in the abstract, is implied by the title. The provided PyTorch implementation is a valuable resource.
Key Takeaways
- •Provides a manual derivation of backpropagation for transformer layers.
- •Includes gradient expressions for LoRA layers.
- •Emphasizes the importance of understanding the backward pass for intuition and debugging.
- •Offers a PyTorch implementation of a GPT-like network.
Reference
“By working through the backward pass manually, we gain a deeper intuition for how each operation influences the final output.”