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Found 18 papers, 13 papers with code
CoFInAl: Enhancing Action Quality Assessment with Coarse-to-Fine Instruction Alignment
However, this common strategy yields suboptimal results due to the inherent struggle of these backbones to capture the subtle cues essential for AQA.
Orchestrate Latent Expertise: Advancing Online Continual Learning with Multi-Level Supervision and Reverse Self-Distillation
However, a notable gap from CL to OCL stems from the additional overfitting-underfitting dilemma associated with the use of rehearsal buffers: the inadequate learning of new training samples (underfitting) and the repeated learning of a few old training samples (overfitting).
MAGR: Manifold-Aligned Graph Regularization for Continual Action Quality Assessment
We propose Continual AQA (CAQA) to refine models using sparse new data.
DualTeacher: Bridging Coexistence of Unlabelled Classes for Semi-supervised Incremental Object Detection
A commonly-used strategy for supervised IOD is to encourage the current model (as a student) to mimic the behavior of the old model (as a teacher), but it generally fails in SSIOD because a dominant number of object instances from old and new classes are coexisting and unlabelled, with the teacher only recognizing a fraction of them.
Pearl: A Production-ready Reinforcement Learning Agent
Reinforcement Learning (RL) offers a versatile framework for achieving long-term goals.
Towards a General Framework for Continual Learning with Pre-training
In this work, we present a general framework for continual learning of sequentially arrived tasks with the use of pre-training, which has emerged as a promising direction for artificial intelligence systems to accommodate real-world dynamics.
Hierarchical Decomposition of Prompt-Based Continual Learning: Rethinking Obscured Sub-optimality
Following these empirical and theoretical insights, we propose Hierarchical Decomposition (HiDe-)Prompt, an innovative approach that explicitly optimizes the hierarchical components with an ensemble of task-specific prompts and statistics of both uninstructed and instructed representations, further with the coordination of a contrastive regularization strategy.
Incorporating Neuro-Inspired Adaptability for Continual Learning in Artificial Intelligence
Continual learning aims to empower artificial intelligence (AI) with strong adaptability to the real world.
SLCA: Slow Learner with Classifier Alignment for Continual Learning on a Pre-trained Model
The goal of continual learning is to improve the performance of recognition models in learning sequentially arrived data.
A Comprehensive Survey of Continual Learning: Theory, Method and Application
To cope with real-world dynamics, an intelligent system needs to incrementally acquire, update, accumulate, and exploit knowledge throughout its lifetime.
PhyGNNet: Solving spatiotemporal PDEs with Physics-informed Graph Neural Network
Solving partial differential equations (PDEs) is an important research means in the fields of physics, biology, and chemistry.
CoSCL: Cooperation of Small Continual Learners is Stronger than a Big One
Continual learning requires incremental compatibility with a sequence of tasks.
Memory Replay with Data Compression for Continual Learning
In this work, we propose memory replay with data compression (MRDC) to reduce the storage cost of old training samples and thus increase their amount that can be stored in the memory buffer.
AFEC: Active Forgetting of Negative Transfer in Continual Learning
Without accessing to the old training samples, knowledge transfer from the old tasks to each new task is difficult to determine, which might be either positive or negative.
Few-shot Continual Learning: a Brain-inspired Approach
Our solution is based on the observation that continual learning of a task sequence inevitably interferes few-shot generalization, which makes it highly nontrivial to extend few-shot learning strategies to continual learning scenarios.
Relaxed Conditional Image Transfer for Semi-supervised Domain Adaptation
Semi-supervised domain adaptation (SSDA), which aims to learn models in a partially labeled target domain with the assistance of the fully labeled source domain, attracts increasing attention in recent years.
ORDisCo: Effective and Efficient Usage of Incremental Unlabeled Data for Semi-supervised Continual Learning
Continual learning usually assumes the incoming data are fully labeled, which might not be applicable in real applications.
Triple Memory Networks: a Brain-Inspired Method for Continual Learning
Continual acquisition of novel experience without interfering previously learned knowledge, i. e. continual learning, is critical for artificial neural networks, but limited by catastrophic forgetting.
