InfoNCE, where NCE stands for Noise-Contrastive Estimation, is a type of contrastive loss function used for self-supervised learning.
Given a set $X = ${$x_{1}, \dots, x_{N}$} of $N$ random samples containing one positive sample from $p\left(x_{t+k}|c_{t}\right)$ and $N − 1$ negative samples from the 'proposal' distribution $p\left(x_{t+k}\right)$, we optimize:
$$ \mathcal{L}_{N} = - \mathbb{E}_{X}\left[\log\frac{f_{k}\left(x_{t+k}, c_{t}\right)}{\sum_{x_{j}\in{X}}f_{k}\left(x_{j}, c_{t}\right)}\right] $$
Optimizing this loss will result in $f_{k}\left(x_{t+k}, c_{t}\right)$ estimating the density ratio, which is:
$$ f_{k}\left(x_{t+k}, c_{t}\right) \propto \frac{p\left(x_{t+k}|c_{t}\right)}{p\left(x_{t+k}\right)} $$
Source: Representation Learning with Contrastive Predictive CodingPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Self-Supervised Learning | 108 | 14.50% |
Image Classification | 26 | 3.49% |
Retrieval | 21 | 2.82% |
Object Detection | 17 | 2.28% |
Semantic Segmentation | 16 | 2.15% |
Self-Supervised Image Classification | 15 | 2.01% |
Classification | 12 | 1.61% |
General Classification | 11 | 1.48% |
Speech Recognition | 10 | 1.34% |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |