mlsanityData to 7 Oct 2026

Self-Supervised learning for Neural Architecture Search (NAS)

arXiv 2304.01023 ↗First posted 3 Apr 2023cs.LGcited by 0

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Abstract (arXiv)

The objective of this internship is to propose an innovative method that uses unlabelled data, i.e. data that will allow the AI to automatically learn to predict the correct outcome. To reach this stage, the steps to be followed can be defined as follows: (1) consult the state of the art and position ourself against it, (2) come up with ideas for development paths, (3) implement these ideas, (4) and finally test them to position ourself against the state of the art, and then start the sequence again. During my internship, this sequence was done several times and therefore gives the tracks explored during the internship.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 29 references that are in the map. First posted on arXiv 3 Apr 2023.

Builds on

17 of 29 references.

References · 17

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  3. 2015SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentationcited by 1,483
  4. 2016Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzlescited by 1,379
  5. 2019A Comprehensive Survey on Transfer Learningcited by 1,154
  6. 2016Colorful Image Colorizationcited by 1,113
  7. 2018Generative Image Inpainting with Contextual Attentioncited by 578
  8. 2020Multi-Task Learning with Deep Neural Networks: A Surveycited by 374
  9. 2018How Does Batch Normalization Help Optimization?cited by 355
  10. 2019S4L: Self-Supervised Semi-Supervised Learningcited by 335
  11. 2019Deep Learning on Image Denoising: An overviewcited by 128
  12. 2020An Overview of Deep Semi-Supervised Learningcited by 121
5 more
  1. 2019ChromaGAN: Adversarial Picture Colorization with Semantic Class Distributioncited by 60
  2. 2018A Trilateral Weighted Sparse Coding Scheme for Real-World Image Denoisingcited by 42
  3. 2020Iterative Bounding Box Annotation for Object Detectioncited by 15
  4. 2020Deep Structured Prediction for Facial Landmark Detectioncited by 6
  5. 2018Do Normalization Layers in a Deep ConvNet Really Need to Be Distinct?cited by 1

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