Catastrophic forgetting is the core problem of class incremental learning (CIL). Existing work mainly adopts memory replay, knowledge distillation, and dynamic architecture to alleviate this problem, but seldom from the aspect of parameter regularization. However, existing parameter regularization methods struggle to achieve an appropriate balance between old and new tasks. To bring it back to CIL, we first propose constrained incremental learning with less forgetting direction (LFD) to leave more plasticity for the new task under a strong stability constraint for old tasks. Specifically, the new parameters are constrained to be close to the LFD of old tasks instead of a single group of old parameters. To validate the effectiveness of this regularization, we investigate the connectivity between the old parameters and the new parameters, and additionally find that a higher accuracy interval exists along the linear connection. Therefore, we further propose a post-processing procedure to find an equilibrium point in this interval for better balance between old and new tasks. Extensive classification experiments on CIFAR-100, ImageNet-100, and ImageNet-1K show our method can significantly improve performance compared with existing CIL methods and the object detection experiments on PASCAL-VOC show its broad generality on other tasks.

Deep networks notoriously suffer from performance deterioration on previous tasks when learning from sequential tasks, i.e., catastrophic forgetting. Recent methods of gradient projection show that the forgetting is resulted from the gradient interference on old tasks and accordingly propose to update the network in an orthogonal direction to the task space. However, these methods assume the task space is invariant and neglect the gradual change between tasks, resulting in sub-optimal gradient projection and a compromise of the continual learning capacity. To tackle this problem, we propose to embed each task subspace into a non-Euclidean manifold, which can naturally capture the change of tasks since the manifold is intrinsically non-static compared to the Euclidean space. Subsequently, we analytically derive the accumulated projection between any two subspaces on the manifold along the geodesic path by integrating an infinite number of intermediate subspaces. Building upon this derivation, we propose a novel geodesic-aligned gradient projection (GAGP) method that harnesses the accumulated projection to mitigate catastrophic forgetting. The proposed method utilizes the geometric structure information on the task manifold by capturing the gradual change between the new and the old tasks. Empirical studies on image classification demonstrate that the proposed method alleviates catastrophic forgetting and achieves on-par or better performance compared to the state-of-the-art approaches.

Distillation strategies are currently the primary approaches for mitigating forgetting in class incremental learning (CIL). Existing methods generally inherit previous knowledge from a single teacher. However, teachers with different mechanisms are talented at different tasks, and inheriting diverse knowledge from them can enhance compatibility with new knowledge. In this paper, we propose the MTD method to find multiple diverse teachers for CIL. Specifically, we adopt weight permutation, feature perturbation, and diversity regularization techniques to ensure diverse mechanisms in teachers. To reduce time and memory consumption, each teacher is represented as a small branch in the model. We adapt existing CIL distillation strategies with MTD and extensive experiments on CIFAR-100, ImageNet-100, and ImageNet-1000 show significant performance improvement. Our code is available at https://github.com/HaitaoWen/CLearning.

Continual learning requires a model to incrementally learn a sequence of tasks and aims to predict well on all the learned tasks so far, which notoriously suffers from the catastrophic forgetting problem. In this paper, we find a new type of bias appearing in continual learning, coined as task-induced bias. We place continual learning into a causal framework, based on which we find the task-induced bias is reduced naturally by two underlying mechanisms in task and domain incremental learning. However, these mechanisms do not exist in class incremental learning (CIL), in which each task contains a unique subset of classes. To eliminate the task-induced bias in CIL, we devise a causal intervention operation so as to cut off the causal path that causes the task-induced bias, and then implement it as a causal debias module that transforms biased features into unbiased ones. In addition, we propose a training pipeline to incorporate the novel module into existing methods and jointly optimize the entire architecture. Our overall approach does not rely on data replay, and is simple and convenient to plug into existing methods. Extensive empirical study on CIFAR-100 and ImageNet shows that our approach can improve accuracy and reduce forgetting of well-established methods by a large margin.

Self-supervised learning has attracted a lot of attention recently, which is able to learn powerful representations without any manual annotations. However, self-supervised learning needs to develop the ability to continuously learn to cope with a variety of real-world challenges, i.e., Continual Self-Supervised Learning (CSSL). Catastrophic forgetting is a notorious problem in CSSL, where the model tends to forget the learned knowledge. In practice, simple rehearsal or regularization will bring extra negative effects while alleviating catastrophic forgetting in CSSL, e.g., overfitting on the rehearsal samples or hindering the model from encoding fresh information. In order to address catastrophic forgetting without overfitting on the rehearsal samples, we propose Augmentation Stability Rehearsal (ASR) in this paper, which selects the most representative and discriminative samples by estimating the augmentation stability for rehearsal. Meanwhile, we design a matching strategy for ASR to dynamically update the rehearsal buffer. In addition, we further propose Contrastive Continuity on Augmentation Stability Rehearsal (C2ASR) based on ASR. We show that C2ASR is an upper bound of the Information Bottleneck (IB) principle, which suggests that C2ASR essentially preserves as much information shared among seen task streams as possible to prevent catastrophic forgetting and dismisses the redundant information between previous task streams and current task stream to free up the ability to encode fresh information. Our method obtains a great achievement compared with state-of-the-art CSSL methods on a variety of CSSL benchmarks.

Class incremental learning (CIL) is one of the most challenging scenarios in continual learning. Existing work mainly focuses on strategies like memory replay, regularization, or dynamic architecture but ignores a crucial aspect: mode connectivity. Recent studies have shown that different minima can be connected by a low-loss valley, and ensembling over the valley shows improved performance and robustness. Motivated by this, we try to investigate the connectivity in CIL and find that the high-loss ridge exists along the linear connection between two adjacent continual minima. To dodge the ridge, we propose parameter-saving OPtimizing Connectivity (OPC) based on Fourier series and gradient projection for finding the low-loss path between minima. The optimized path provides infinite low-loss solutions. We further propose EOPC to ensemble points within a local bent cylinder to improve performance on learned tasks. Our scheme can serve as a plug-in unit, extensive experiments on CIFAR-100, ImageNet-100, and ImageNet-1K show consistent improvements when adapting EOPC to existing representative CIL methods. Our code is available at https://github.com/HaitaoWen/EOPC.

Many existing methods focus on detecting either objects from different domains or those of rare classes, but it's difficult for them to tackle the two issues together. However, in the real world, due to the difficulty of collecting samples of special classes, deep learning practitioners have to use simulated images to substitute for them. To deal with this scenario, in this paper, we research a new task: cross-domain object detection with missing classes in target domain, where there are only partial classes have images and annotations in the target domain. We devise a simple but effective play-and-plug method to address this new task, named the three-stage learning approach with domain and class information preservation. In addition, extensive experiments demonstrate our method is effective and can boost the performance when added to existing unsupervised domain adaptation object detectors.

This paper presents a T-type three-level LLC converter used in the wide input voltage range. At the same time, by combining frequency control, phase control and mode control together, a hybrid control strategy applied to wide input applications is proposed in this paper. Compared with the traditional LLC converter, the hybrid control strategy makes the gain ratio of the LLC reach more than 8 times. This paper introduces the topology and case analysis, and the results are given based on the experiments.