The End Of Knowledge - Vault 6/69 - CVPR - 2021 - Understanding self-supervised learning dynamics without contrastive pairs

graph LR classDef ssl fill:#f9d4d4, font-weight:bold, font-size:14px classDef analysis fill:#d4f9d4, font-weight:bold, font-size:14px classDef methods fill:#d4d4f9, font-weight:bold, font-size:14px classDef performance fill:#f9f9d4, font-weight:bold, font-size:14px A[Understanding self-supervised learning
dynamics without contrastive
pairs] --> B[Self-supervised
Learning] A --> C[Analysis
and
Understanding] A --> D[SSL
Methods] A --> E[Performance
and
Applications] B --> B1[Learns from
data
augmentation. 1] B --> B2[No negative
pairs
needed. 2] B --> B3[Simple model
for SSL
dynamics. 3] B --> B4[Prevents gradient
flow in target
network. 4] B --> B5[Prevents collapse
in non-contrastive
SSL. 5] B --> B6[Simplifies data
and augmentation
distributions. 6] C --> C1[Inspired by training
observations. 7] C --> C2[Simplified training
process equations. 8] C --> C3[Gradual alignment
during
training. 9] C --> C4[Simplified analysis
post-alignment. 10] C --> C5[System dynamics
visualization. 11] C --> C6[Region leading
to
collapse. 12] D --> D1[Novel non-contrastive
SSL
method. 17] D --> D2[Estimates correlation
matrix. 18] D --> D3[Obtains eigenvalues
and
eigenvectors. 19] D --> D4[Sets predictor
based on
matrix. 20] D --> D5[Combines DirectPredict
with gradient
updates. 22] D --> D6[Matches or exceeds
BYOL. 23] E --> E1[Strong results on
multiple
datasets. 21] E --> E2[Minimal linear setting
study. 24] E --> E3[Effects on training
dynamics. 25] E --> E4[Open-source DirectPredict
implementation. 26] E --> E5[Performance comparison
of predictors. 27] E --> E6[Insights on SSL
functionality. 28] class A,B,B1,B2,B3,B4,B5,B6 ssl class C,C1,C2,C3,C4,C5,C6 analysis class D,D1,D2,D3,D4,D5,D6 methods class E,E1,E2,E3,E4,E5,E6 performance

Resume:

1.- Self-supervised learning: Learns representations from data augmentation without labeled datasets, useful in NLP, speech, and computer vision.

2.- Non-contrastive SSL: Doesn't require negative pairs, raising questions about why it doesn't collapse to trivial solutions.

3.- Minimum model: Simple linear model to study non-contrastive SSL dynamics, using online and target networks with a predictor.

4.- Stop gradient: Technique preventing gradient flow through the target network.

5.- Predictor importance: Essential component in non-contrastive SSL to prevent collapse.

6.- Isotropic assumptions: Simplifying assumptions about data and augmentation distributions for analysis.

7.- Symmetric predictor: Assumption inspired by empirical observations during training.

8.- Reduced dynamics: Simplified equations describing the training process under stated assumptions.

9.- Eigenspace alignment: Gradual alignment of predictor and correlation matrix eigenspaces during training.

10.- Decoupled dynamics: Simplified 1D scalar case analysis after eigenspace alignment.

11.- Phase diagram: Visual representation of system dynamics, showing trivial and non-trivial basins.

12.- Trivial basin: Region where initialization leads to collapse (trivial solution).

13.- Non-trivial basin: Region where initialization leads to meaningful representations.

14.- Weight decay effects: Influences trivial basin size and eigenspace alignment.

15.- Relative learning rate: Affects trivial basin size and eigenspace alignment condition.

16.- Exponential moving average: Impacts eigenspace alignment and training speed.

17.- DirectPredict: Novel non-contrastive SSL method aligning predictor with correlation matrix eigenspace.

18.- Online estimation: Technique to estimate correlation matrix for nonlinear networks.

19.- Eigendecomposition: Process to obtain eigenvalues and eigenvectors of estimated correlation matrix.

20.- Predictor construction: Setting predictor eigenvalues and eigenvectors based on correlation matrix and discovered invariance.

21.- Empirical performance: DirectPredict shows strong results on CIFAR-10, STL-10, and ImageNet.

22.- Hybrid approach: Combining DirectPredict with gradient updates for improved performance.

23.- ImageNet results: DirectPredict matches or exceeds BYOL performance with simpler predictor architecture.

24.- Systematic analysis: Study of non-contrastive SSL dynamics using minimal linear setting.

25.- Hyperparameter roles: Understanding effects of various hyperparameters on training dynamics.

26.- Code availability: Open-source implementation of DirectPredict.

27.- Linear vs. nonlinear predictors: Comparison of performance between simple linear and complex nonlinear predictors.

28.- Theoretical implications: Insights into why non-contrastive SSL works and doesn't collapse.

29.- Practical applications: Potential for improving AI systems using massive unlabeled datasets.

30.- Future research: Opening doors for further investigation into self-supervised learning dynamics.

Knowledge Vault built byDavid Vivancos 2024