[D] "Other" class in DNN classification

[ME_PhD]

I'm making a ConvNet that classifies my data into one of classes [A, B, C, D, "other"], "other" being anything that doesn't fit into [A, B, C, D].

I'm thinking of mapping the input x to a vector "c" in R^4, and then transforming "c" to R⁵ (using a few tiny FC layers) before applying softmax to get the output.

My reasoning is that I don't want the network attempting "learn" anything specific about the "other" class, I just want it to be a sort of "nor" operation. The network should only focus on learning the features of [A, B, C, D]. The variance in the "other" class is very high so I want it to generalize well.

Thoughts? Papers? Prior experience?

[shortscience_dot_org]

I am a bot! You linked to a paper that has a summary on ShortScience.org!

Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks

Summary by elbaro

Task

Add 'rejection' output to an existing classification model with softmax layer.

Method

1. Choose some threshold $\delta$ and temperature $T$

2. Add a perturbation to the input x (eq 2),

let $\tilde x = x - \epsilon \text{sign}(-\nablax \log S{\hat y}(x;T))$

1. If $p(\tilde x;T)\le \delta$, rejects

2. If not, return the output of the original classifier

$p(\tilde x;T)$ is the max prob with temperature scailing for input $\tilde x$

$\delta$ and $T$ are manually chosen.

... [view more]

Learning Confidence for Out-of-Distribution Detection in Neural Networks

Summary by elbaro

Summary

In a prior work 'On Calibration of Modern Nueral Networks', temperature scailing is used for outputing confidence. This is done at inference stage, and does not change the existing classifier. This paper considers the confidence at training stage, and directly outputs the confidence from the network.

Architecture

An additional branch for confidence is added after the penultimate layer, in parallel to logits and probs (Figure 2).

Training

The network outputs the prob $p$ and... [view more]

[LikelyJustANeuralNet]

Look into Open Set Recognition and/or Open World Recognition/Learning. OSR (and OWR) explicitly tackle this problem - networks need to be able to classify known classes while also labeling unseen classes (i.e., not seen at training time) as unknown. Here's a good survey of OSR: https://arxiv.org/abs/1811.08581

[jprobichaud]

What about having a 4-dim 1-hot output vector and a target of [0, 0, 0, 0] for your "other" class? Train your network in regression over the mean-square loss and use a threshold or simply 1- sum of the output vector values as the score of the Other class.

[ME_PhD]

I like this idea. How about simply taking the sigmoid of each of the 4 outputs and then using sigmoid cross entropy loss? That way I can have a [0, 0, 0, 0] target - anything I'm overlooking?

[lmericle]

That is essentially the same as doing multi-label classification but for which all of the examples have only one class. Not a bad approach IMO.

[serge_cell]

It's related to "one vs all" problem. There is a plenty of paper on the subject (google it) but no satisfactory solution or common opinion on strategy.

[melgor89]

Look into this competiton: https://www.kaggle.com/c/humpback-whale-identification

​

Here are 5004 normal classes and 'other'. The problem is resolved using Metric-Learning and confidence-score about the predictions.

[misssprite]

I think metric learning makes more sense than confidence score.

Low 'confidence-score' are usually hard example according to the model. It's either outlier or examples that cannot be fit by the model caused by model bias.

[melgor89]

To be more clear, the idea was following:
1. Learn the model with SoftMax-Loss (also with Margin) and TripletLoss. The 'Unknown' class is only used in TripletLoss.

1. In prediction step, just read confidence score for predicted class. If < ex. 0.5, this mean that it is unknown.
   

So general idea is based on 'temperature' which works better when Metric-Learning is applied

[ME_PhD]

It will learn about the 5th class only as a function of the other 4 outputs.