# From Scores to Predictions in Multi-Label Classification: Neural Thresholding Strategies
## 3 Related Works
### Newer Approaches to Obtaining Multi-Label Predictions  
Apart from thresholding techniques, more sophisticated ways for obtaining multi-label predictions from class scores can be found in papers discussed in this section. They are not meant to be replacements for the classic thresholding methods presented above, but their objectives are often related, i.e., to obtain multi-label predictions that take into account the relationships between label scores. Most of the methods emit either 0 or 1 for a given label basing the decision not only on the score (its value or rank) for this particular label but taking into account the scores of other labels, similar to what our method does. Ref. [36] proposes an algorithm that jointly trains a chain of per-label classifiers, where every next classifier has a vector of class probabilities provided as a part of its input feature vector. This _predictions-as-features_ approach allows the classifiers to incorporate knowledge about decisions made by peer classifiers allowing them to model dependencies between labels. In a meta-learning approach for multi-label classification proposed by [37], a recurrent neural network (RNN) is trained alongside a base classifier. In each training step, this recurrent meta-learner returns a vector of thresholds that is then used by the base classifier to produce final results; moreover, these thresholds are included as part of the input vector for calculating new threshold values in the subsequent time step. Similar to our approach, the meta-learning technique is model-agnostic--a base classifier could be any model that returns score vectors--but here, both the base classifier and the meta-learner have to be trained jointly, which is not the case in our method. Perhaps the closest to our proposition is the _rethinking structure_ introduced in [38]. There, an RNN model iteratively refines the predictions for a couple of steps in a sequence. Firstly, a multi-label soft prediction vector is obtained from a feature vector only (the scoring phase); then, in each step of a rethinking process, newer predictions are calculated from previous ones, forming a repeating re-scoring procedure, which ends in the final thresholded predictions. The re-scoring is conducted via a memory transformation matrix that produces an \(L\)-sized soft prediction vector out of the previous state of the same vector. One step of this rethinking process is similar to using a naive version of our model (Figure 1b, described later). The crucial difference is that our models are much more scalable, because, firstly, we deliberately sidestep the use of a direct transformation matrix with \(O(L^{2})\) complexity, and, secondly, we drop the recursive process of the refinement, forcing all dependencies between scores to be consumed in a short forward pass of the model. An interesting use case for thresholding methods was presented in [39], where they were used to obtain _pseudo-labeling_ in semi-supervised multi-label classification tasks. To obtain positive or negative annotation for a given unlabeled sample-label pair, two score thresholds per class are used. If a score exceeds a _positive threshold_ or is lower than a _negative threshold_, then this pair is annotated suitably (with 1 or 0, respectively); otherwise, it remains unlabeled. These two score thresholds for each class are calculated dynamically based on chosen global positive and negative _percentile-based_ thresholds and per-label score distributions among samples in a training batch. Finally, it is worth mentioning attention-based classification heads of object detectors for images. The modules, introduced in [40], and later refined in [41], produce a prediction for each label whose embedding appears in its input sequence of embeddings. These label embeddings (i.e., per-label trainable parameters initialized randomly or obtained from label titles via NLP techniques) are compared via attention to the additional context, which are image embeddings from a backbone convolutional neural network.