# Direction Concentration Learning: Enhancing Congruency in Machine Learning

arXiv: https://arxiv.org/abs/1912.08136 · HTML: https://mlsanity.com/paper/1912.08136/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2019.

## Built on by (most cited first, 5 of 5)

- [Stable Neural ODE with Lyapunov-Stable Equilibrium Points for Defending Against Adversarial Attacks](https://mlsanity.com/paper/2110.12976/) (2021)
- [Learning to Predict Gradients for Semi-Supervised Continual Learning](https://mlsanity.com/paper/2201.09196/) (2022)
- [Astroformer: More Data Might not be all you need for Classification](https://mlsanity.com/paper/2304.05350/) (2023)
- [Personalizing Pre-trained Models](https://mlsanity.com/paper/2106.01499/) (2021)
- [Learning to Minimize the Remainder in Supervised Learning](https://mlsanity.com/paper/2201.09193/) (2022)

## Abstract (arXiv)

One of the well-known challenges in computer vision tasks is the visual diversity of images, which could result in an agreement or disagreement between the learned knowledge and the visual content exhibited by the current observation. In this work, we first define such an agreement in a concepts learning process as congruency. Formally, given a particular task and sufficiently large dataset, the congruency issue occurs in the learning process whereby the task-specific semantics in the training data are highly varying. We propose a Direction Concentration Learning (DCL) method to improve congruency in the learning process, where enhancing congruency influences the convergence path to be less circuitous. The experimental results show that the proposed DCL method generalizes to state-of-the-art models and optimizers, as well as improves the performances of saliency prediction task, continual learning task, and classification task. Moreover, it helps mitigate the catastrophic forgetting problem in the continual learning task. The code is publicly available at https://github.com/luoyan407/congruency.