adding details to NTXentLoss documentation

Author: Jordan Stomps

docs/losses.md

@@ -787,6 +787,13 @@ This is also known as InfoNCE, and is a generalization of the [NPairsLoss](losse
  - [Representation Learning with Contrastive Predictive Coding](https://arxiv.org/pdf/1807.03748.pdf){target=_blank}
  - [Momentum Contrast for Unsupervised Visual Representation Learning](https://arxiv.org/pdf/1911.05722.pdf){target=_blank}
  - [A Simple Framework for Contrastive Learning of Visual Representations](https://arxiv.org/pdf/2002.05709.pdf){target=_blank}
+
+In the equation below, loss is computed for each positive pair, `k_+`, in a batch, normalized by all pairs in the batch, `k_i in K`.
+For each `embeddings` with `labels` and `ref_emb` with `ref_labels`, positive pair `(embeddings[i], ref_emb[j])` are defined when `labels[i] == ref_labels[j]`.
+When `embeddings` and `ref_emb` are augmented versions of each other (e.g. SimCLR), `labels[i] == ref_labels[i]` (see [SelfSupervisedLoss](losses.md#selfsupervisedloss)).
+Note that multiple positive pairs can exist if the same label is present multiple times in `labels` and/or `ref_labels`.
+
+Instead of passing labels (`NTXentLoss(embeddings, labels, ref_emb=ref_emb, ref_labels=ref_labels)`), `indices_tuple` could be passed (see [`pytorch_metric_learning.utils.loss_and_miner_utils.get_all_pairs_indices](https://github.com/KevinMusgrave/pytorch-metric-learning/blob/master/src/pytorch_metric_learning/utils/loss_and_miner_utils.py)).
 ```python
 losses.NTXentLoss(temperature=0.07, **kwargs)
 ```
@@ -799,6 +806,16 @@ losses.NTXentLoss(temperature=0.07, **kwargs)
 
 * **temperature**: This is tau in the above equation. The MoCo paper uses 0.07, while SimCLR uses 0.5.
 
+**Other info:**
+
+For example, consider `labels = ref_labels = [0, 0, 1, 2]`. Two losses will be computed:
+
+* Positive pair of indices `[0, 1]`, with negative pairs of indices `[0, 2], [0, 3]`.
+
+* Positive pair of indices `[1, 0]`, with negative pairs of indices `[1, 2], [1, 3]`.
+
+Labels `1`, and `2` do not have positive pairs, and therefore the negative pair of indices `[2, 3]` will not be used.
+
 **Default distance**: 
 
  - [```CosineSimilarity()```](distances.md#cosinesimilarity)