Deep learning notes 05: unsupervised vision with GLOM

#papers

This post if from a series of quick notes written primarily for personal usage while reading random ML/SWE/CS papers. As such they might be incomprehensible and/or flat out wrong.

DINO: Emerging Properties in Self-Supervised Vision Transformers

  • Unsupervised learning regime for vision (self attention, 8x8 patches) transformers
    • Intermediate representation clusters pictures of similar labels together (without seeing labels)
    • Capable of object detection and masking (attention mask segments objects very well)
    • Capable of classification (output KNN to known labeled examples)
    • Copy detection, image retrieval, … -> good similarity measure
  • Attention masks for CLS token: the token that contains final representation (doesn’t have image patch on input, to not bias)
  • Self-supervised learning: self-distillation without labels
  • Negative samples learning:
    • Take anchor patch and patch A from one image, and patch B from second image
    • Give all three patches to the model, tell it which is anchor patch
    • Ask whether A or B is from the same as anchor
  • Self-supervised without negative samples learning
    • Use only one image, augment in multiple ways (BYOL) -> produce two versions for teacher and student
      • Global crops: > 50 % of the image
      • Local crops: < 50 % of the image
      • Rotations, color-jitters, …
    • Pass each one version through teacher, one through student
      • Note: Actually pass both through both, loss is combination of cross-difference
    • Loss is the difference between end image representations (CLS output)
      • Same image, only differently augmented -> should have similar representation
      • To mitigate collapse to single repre. -> different models for teacher and student
    • Only train (backprop) student, build teacher as exponential average of students
  • Teacher only uses global cropping
    • If student has local crop -> student learns that its patch should match the whole with more context
    • -> forces the model to learn part-whole relationship & representing the whole image
  • Teacher maintains running average of all representations it sees -> subtracts it from its representation
    • ~normalization, helps against collapse
  • Representation has softmax with temperature at the end
    • Dimensionality of softmax is arbitrary: don’t have explicit labels (unsupervised) -> who knows how many
    • Teacher has sharpening -> more peaked distribution -> forcer larger differences between diff. outputs
    • Softmax is not common in unsupervised -> forces model to come up with “its own classes”
  • Versus supervised learning
    • Supervised has way more noisy / overfitted attention mask -> hyper optimization on the task at hand
  • Why does it work?
    • Augmentations: in computer vision they’re super important ~ that’s where the human prior is
      • What’s augmented away doesn’t matter
  • Dataset: there’s always an explicit object of interest -> how we take pictures brings prior
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