Dark Experience for General Continual Learning: a Strong, Simple Baseline

• URL: http://arxiv.org/abs/2004.07211v2
• Date: Thu, 22 Oct 2020 14:00:23 GMT
• Title: Dark Experience for General Continual Learning: a Strong, Simple Baseline
• Authors: Pietro Buzzega, Matteo Boschini, Angelo Porrello, Davide Abati, Simone Calderara
• Abstract summary: We work towards General Continual Learning (GCL), where task boundaries blur and the domain and class distributions shift either gradually or suddenly. We address it through mixing rehearsal with knowledge distillation and regularization; our simple baseline, Dark Experience Replay, matches the network's logits sampled throughout the optimization trajectory. By conducting an extensive analysis on both standard benchmarks and a novel GCL evaluation setting (MNIST-360), we show that such a seemingly simple baseline outperforms consolidated approaches.
• Score: 18.389103500859804
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Continual Learning has inspired a plethora of approaches and evaluation settings; however, the majority of them overlooks the properties of a practical scenario, where the data stream cannot be shaped as a sequence of tasks and offline training is not viable. We work towards General Continual Learning (GCL), where task boundaries blur and the domain and class distributions shift either gradually or suddenly. We address it through mixing rehearsal with knowledge distillation and regularization; our simple baseline, Dark Experience Replay, matches the network's logits sampled throughout the optimization trajectory, thus promoting consistency with its past. By conducting an extensive analysis on both standard benchmarks and a novel GCL evaluation setting (MNIST-360), we show that such a seemingly simple baseline outperforms consolidated approaches and leverages limited resources. We further explore the generalization capabilities of our objective, showing its regularization being beneficial beyond mere performance.

Related papers

• In-Context Linear Regression Demystified: Training Dynamics and Mechanistic Interpretability of Multi-Head Softmax Attention [52.159541540613915]
  We study how multi-head softmax attention models are trained to perform in-context learning on linear data. Our results reveal that in-context learning ability emerges from the trained transformer as an aggregated effect of its architecture and the underlying data distribution.
  arXiv  Detail & Related papers  (2025-03-17T02:00:49Z)
• Continual Task Learning through Adaptive Policy Self-Composition [54.95680427960524]
  CompoFormer is a structure-based continual transformer model that adaptively composes previous policies via a meta-policy network. Our experiments reveal that CompoFormer outperforms conventional continual learning (CL) methods, particularly in longer task sequences.
  arXiv  Detail & Related papers  (2024-11-18T08:20:21Z)
• Zero-Shot Generalization during Instruction Tuning: Insights from Similarity and Granularity [84.12126298229866]
  We show that zero-shot generalization during instruction tuning happens very early. We also show that encountering highly similar and fine-grained training data earlier during instruction tuning, without the constraints of defined "tasks", enables better generalization. For the first time, we show that zero-shot generalization during instruction tuning is a form of similarity-based generalization between training and test data at the instance level.
  arXiv  Detail & Related papers  (2024-06-17T16:40:21Z)
• Offline Reinforcement Learning from Datasets with Structured Non-Stationarity [50.35634234137108]
  Current Reinforcement Learning (RL) is often limited by the large amount of data needed to learn a successful policy. We address a novel Offline RL problem setting in which, while collecting the dataset, the transition and reward functions gradually change between episodes but stay constant within each episode. We propose a method based on Contrastive Predictive Coding that identifies this non-stationarity in the offline dataset, accounts for it when training a policy, and predicts it during evaluation.
  arXiv  Detail & Related papers  (2024-05-23T02:41:36Z)
• Pay Attention to Your Neighbours: Training-Free Open-Vocabulary Semantic Segmentation [19.20874993309959]
  vision-language foundation models, such as CLIP, have showcased remarkable effectiveness in numerous zero-shot image-level tasks. We propose a baseline for training-free OVSS, termed Neighbour-Aware CLIP (NACLIP) Our method enforces localization of patches in the self-attention of CLIP's vision transformer which, despite being crucial for dense prediction tasks, has been overlooked in the OVSS literature.
  arXiv  Detail & Related papers  (2024-04-12T01:08:04Z)
• Random Representations Outperform Online Continually Learned Representations [68.42776779425978]
  We show that existing online continually trained deep networks produce inferior representations compared to a simple pre-defined random transforms. Our method, called RanDumb, significantly outperforms state-of-the-art continually learned representations across all online continual learning benchmarks. Our study reveals the significant limitations of representation learning, particularly in low-exemplar and online continual learning scenarios.
  arXiv  Detail & Related papers  (2024-02-13T22:07:29Z)
• Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations [58.758928936316785]
  offline reinforcement learning from visual observations with continuous action spaces remains under-explored. We show that modifications to two popular vision-based online reinforcement learning algorithms suffice to outperform existing offline RL methods.
  arXiv  Detail & Related papers  (2022-06-09T22:08:47Z)
• Generalized Variational Continual Learning [33.194866396158005]
  Two main approaches to continuous learning are Online Elastic Weight Consolidation and Variational Continual Learning. We show that applying this modification to mitigate Online EWC as a limiting case, allowing baselines between the two approaches. In order to the observed overpruning effect of VI, we take inspiration from a common multi-task architecture, mitigate neural networks with task-specific FiLM layers.
  arXiv  Detail & Related papers  (2020-11-24T19:07:39Z)
• Reinforcement Learning for Weakly Supervised Temporal Grounding of Natural Language in Untrimmed Videos [134.78406021194985]
  We focus on the weakly supervised setting of this task that merely accesses to coarse video-level language description annotation without temporal boundary. We propose a emphBoundary Adaptive Refinement (BAR) framework that resorts to reinforcement learning to guide the process of progressively refining the temporal boundary.
  arXiv  Detail & Related papers  (2020-09-18T03:32:47Z)
• Few-Shot Unsupervised Continual Learning through Meta-Examples [21.954394608030388]
  We introduce a novel and complex setting involving unsupervised meta-continual learning with unbalanced tasks. We exploit a meta-learning scheme that simultaneously alleviates catastrophic forgetting and favors the generalization to new tasks. Experimental results on few-shot learning benchmarks show competitive performance even compared to the supervised case.
  arXiv  Detail & Related papers  (2020-09-17T07:02:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.