An unintended consequence of feature sharing is the model fitting to correlated tasks within the dataset, termed negative transfer.   In this paper, we revisit the problem of negative transfer in multitask setting and find that its corrosive effects are applicable to a wide range of linear and non-linear models, including neural networks. We first study the effects of negative transfer in a principled way and show that previously proposed counter-measures are insufficient, particularly for trainable features. We propose an adversarial training approach to mitigate the effects of negative transfer by viewing the problem in a domain adaptation setting. Finally, empirical results on attribute prediction multi-task on AWA and CUB datasets further validate the need for correcting negative sharing in an end-to-end manner. Advances in machine learning have led to proficient supervised learning models with powerful representations in various prediction tasks. We now expect an ideal classification model to restrict itself to a pertinent set of evidences available to it from the input for prediction. Further, we expect the model to disregard any unrelated evidences in the data to enable better generalization. Figure 1: A supervised classifier 'cheetah vs. snow-leopard' that uses unrelated evidence (of habitat) over relevant evidence (of fur patterns). As shown by the pixel importance maps, the model suffers from the negative transfer prevalent in a typical animal image dataset skewed towards the animal's typical habitat and fails to generalize to rare samples.Let us consider the task of training an animal classifier "cheetah vs. snow-leopards" from a dataset of images of these animals, such as those illustrated in Figure 1 -a task which ideally should focus on the animal's appearance features. However, a large portion of these images also contain various cues of the typical habitat of the animals in the background, i.e., tall grass and snow (see Figures 1 (a) and (b)) which are, in principle, unrelated to the animal's appearance. An archetypal model is deceived by the co-occurrence of such unrelated, yet easily detectable cues of habitat over the animal's appearance features such as complex fur patterns. However, a proficient supervised learning model must identify relevant evidences for the label of interest and at the same time discard various unrelated evidences such as presence of snow, even though it tends to co-occur frequently with snow-leopard. Consequently, it would be more likely that such a model would perform better on rare-instances (such as those in Figures 1 (c) and (d)) and generalize better to unseen instances.This phenomenon of co-occurring but unrelated evidences being present in training data and thereby having a debilitating effect on model performance has been described in literature BID8 ; BID16 ; BID9 ; BID13 ; BID15 ). These techniques utilize the easy availability of labels for unrelated evidences (e.g. background habitat labels above), called negative labels which constitutes an auxiliary task, and seek to mitigate its debilitating performance on the primary task (e.g. animal classification above) with techniques referred to as negative-sharing or negative-transfer.While all of these techniques have tackled this problem utilizing various forms of regularization, we describe several shortcomings of this class of approaches, most notable of which is their inapplicability to the popular paradigm of trainable features obtained via neural representation learning. Motivated by these limitations, in this paper we depart from the direction of regularization-based approaches and examine methods inspired from a domain-adaptation viewpoint to propose an adversarial training-based formulation. We uniquely view such a scenario as an instance of adversarial multi-task learning, where the classification tasks are either the primary task of interest (i.e., predicting the presence of fur pattern and color) or the auxiliary negative tasks (i.e., characteristics of habitat) to be avoided. Since the 2 tasks are unrelated, any label correlation between primary and auxiliary labels in the training data is only by chance and therefore from a domain-adaptation perspective, we envision a target-domain as possibly having a different correlation between the primary and auxiliary labels. The effects of negative transfer are hence mitigated when the classification task is trained in this domain.We discuss advantages of our proposed formulation, inspired from domain-adaptation, to alleviate the negative transfer over existing techniques, including ready applicability to neural networks in an end-to-end fashion. It must be noted that, while the formulation of the problem is motivated with multi-task learning, negative-transfer is a disposition of any supervised learning task from simple binary classification to recent popular supervised tasks such as image detection, captioning, or visual dialog. We present motivating literature that prelude this work next. In this work, we show that adversarial learning is the natural answer to prevent negative transfer. This leads to potential improvement in any supervised learning of natural data that is seeking generalization. We find that even in relatively straight-forward linear models presented above, co-occurrence of unrelated labels hampers performance and must be explicitly treated. We address the problem of negative transfer in a multi-task scenario, and also show the applicability of our solution in any supervised task. Supervised learning practitioners can utilize domain expertise to acquire and leverage additional negative labels for this purpose. Recent work in explainability of machine learning models can also be appropriately leveraged to facilitate this task.
We look at negative transfer from a domain adaptation point of view to derive an adversarial learning algorithm.
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