References & Further Reading

References

1. J. Yosinski et al., How Transferable Are Features in Deep Neural Networks?, NeurIPS, 2014

   Quantifies feature transferability across layers and tasks.

2. E. J. Hu et al., LoRA: Low-Rank Adaptation of Large Language Models, ICLR, 2021

   Introduces LoRA for parameter-efficient fine-tuning.

3. K. Zhang et al., Plug-and-Play Image Restoration with Deep Denoiser Prior, IEEE TPAMI, 2021

   PnP framework using DRUNet for inverse problems.

4. Y. Ganin et al., Domain-Adversarial Training of Neural Networks, JMLR, 2016

   Domain adaptation via adversarial feature alignment.

Further Reading

• torchvision model zoo

  https://pytorch.org/vision/stable/models.html

  Complete list of pre-trained models with usage examples.

• ONNX Runtime

  https://onnxruntime.ai/

  High-performance inference engine for ONNX models.

• HuggingFace PEFT library

  https://github.com/huggingface/peft

  LoRA, QLoRA, and other parameter-efficient fine-tuning methods.