The G-ACIL is a general version of the ACIL for the general CIL setting. For the tradition CIL setting, the G-ACIL is equivalent to the ACIL. Thus, we use the same implementation in this repository.

2022年5月30日 · Inspired by linear learning formulations, we propose an analytic class-incremental learning (ACIL) with absolute memorization of past knowledge while avoiding breaching of data privacy (i.e., without storing historical data).

Inspired by linear learning formula-tions, we propose an analytic class-incremental learning (ACIL) with absolute memorization of past knowledge while avoiding breaching of data privacy (i.e., without storing historical data).

The learning paradigm is called Class-Incremental Learning (CIL). We propose a Python toolbox that implements several key algorithms for class-incremental learning to ease the burden of researchers in the machine learning community.

Class-incremental learning (CIL) learns a classification model with training data of different classes arising progressively. Existing CIL either suffers from serious accuracy loss due to catastrophic forgetting, or invades data privacy by revisiting used exemplars.

In this paper, we propose an analytic class-incremental learning (ACIL) to handle the issue of forgetting and privacy invasion in CIL. The ACIL is inspired by analytic learning [11, 12], a technique that formulates network training into learning of linear stacks.

Inspired by learning of linear problems, we propose an analytic class-incremental learning (ACIL) with absolute memorization of past knowledge while avoiding breaching of data privacy (i.e., without storing historical data).

In this paper, we introduce ACIL, a novel Active learning framework for Class Incremental Learning. Contrary to existing CIL settings, we assume that in each episode, only a small portion of the data is annotated; majority of the samples are unlabeled in each episode. In

Inspired by linear learning formulations, we propose an analytic class-incremental learning (ACIL) with absolute memorization of past knowledge while avoiding breaching of data privacy (i.e., without storing historical data).

2022年5月30日 · Inspired by linear learning formulations, we propose an analytic class-incremental learning (ACIL) with absolute memorization of past knowledge while avoiding breaching of data privacy (i.e., without storing historical data).