mlsanityData to 7 Oct 2026

Neural Network Libraries: A Deep Learning Framework Designed from Engineers' Perspectives

arXiv 2102.06725 ↗First posted 12 Feb 2021cs.LG cs.CVcited by 6

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Abstract (arXiv)

While there exist a plethora of deep learning tools and frameworks, the fast-growing complexity of the field brings new demands and challenges, such as more flexible network design, speedy computation on distributed setting, and compatibility between different tools. In this paper, we introduce Neural Network Libraries (https://nnabla.org), a deep learning framework designed from engineer's perspective, with emphasis on usability and compatibility as its core design principles. We elaborate on each of our design principles and its merits, and validate our attempts via experiments.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 24 of its 32 references that are in the map. First posted on arXiv 12 Feb 2021.

Builds on

24 of 32 references.

References · 24

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2020Language Models are Few-Shot Learnerscited by 32,199
  4. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  5. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
  6. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  7. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  8. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  9. 2017Squeeze-and-Excitation Networkscited by 4,763
  10. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
  11. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  12. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,580
12 more
  1. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,967
  2. 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,540
  3. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,048
  4. 2019Searching for MobileNetV3cited by 1,984
  5. 2018ESRGAN: Enhanced Super-Resolution Generative Adversarial Networkscited by 1,250
  6. 2017Mixed Precision Trainingcited by 1,135
  7. 2017StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translationcited by 1,036
  8. 2014Caffe: Convolutional Architecture for Fast Feature Embeddingcited by 287
  9. 2017State-of-the-art Speech Recognition With Sequence-to-Sequence Modelscited by 196
  10. 2018ReenactGAN: Learning to Reenact Faces via Boundary Transfercited by 85
  11. 2019Data Cleansing for Models Trained with SGDcited by 67
  12. 2018Learning Temporal Coherence via Self-Supervision for GAN-based Video Generationcited by 18

Built on by

Cited by year

Citing papers per year: 2021: 2; 2022: 3; 2023: 1.

6 papers.

  1. 2022Fine-grained Image Editing by Pixel-wise Guidance Using Diffusion Modelscited by 18
  2. 2022A Low Memory Footprint Quantized Neural Network for Depth Completion of Very Sparse Time-of-Flight Depth Mapscited by 6
  3. 2021Data Cleansing for Deep Neural Networks with Storage-efficient Approximation of Influence Functionscited by 2
  4. 2023NDJIR: Neural Direct and Joint Inverse Rendering for Geometry, Lights, and Materials of Real Objectcited by 1
  5. 2021Latte: Cross-framework Python Package for Evaluation of Latent-Based Generative Modelscited by 0
  6. 2022Thinking the Fusion Strategy of Multi-reference Face Reenactmentcited by 0