This work investigates the entanglement between Continual Learning (CL) and Transfer Learning (TL). In particular, we shed light on the widespread application of network pretraining, highlighting that it is itself subject to catastrophic forgetting. Unfortunately, this issue leads to the under-exploitation of knowledge transfer during later tasks. On this ground, we propose Transfer without Forgetting (TwF), a hybrid approach building upon a fixed pretrained sibling network, which continuously propagates the knowledge inherent in the source domain through a layer-wise loss term. Our experiments indicate that TwF steadily outperforms other CL methods across a variety of settings, averaging a 4.81% gain in Class-Incremental accuracy over a variety of datasets and different buffer sizes. Our code is available at https://github.com/mbosc/twf.

One of the measures of the health of a deep learning project is the degree to which it utilizes the training resources that it was allocated. Whether you are training in the cloud or on your own private infrastructure, training resources cost money, and any block of time in which they are left idle represents a potential opportunity to increase training throughput and overall productivity. This is particularly true for the training accelerator -- typically the most expensive training resource -- whether it be a GPU, a Google TPU, or a Habana Gaudi. This blog is a sequel to a previous post on the topic of Overcoming Data Preprocessing Bottlenecks in which we addressed the undesired scenario in which your training accelerator, henceforth assumed to be a GPU, finds itself idle while it waits for data input from an overly tasked CPU. The post covered several different ways of addressing this type of bottleneck and demonstrated them on a toy example, all the while emphasizing that the best option would very much depend on the specifics of the model and project at hand.

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Training materials reviewed by The Intercept confirm that Google is offering advanced artificial intelligence and machine-learning capabilities to the Israeli government through its controversial "Project Nimbus" contract. The Israeli Finance Ministry announced the contract in April 2021 for a $1.2 billion cloud computing system jointly built by Google and Amazon. "The project is intended to provide the government, the defense establishment and others with an all-encompassing cloud solution," the ministry said in its announcement. Google engineers have spent the time since worrying whether their efforts would inadvertently bolster the ongoing Israeli military occupation of Palestine. In 2021, both Human Rights Watch and Amnesty International formally accused Israel of committing crimes against humanity by maintaining an apartheid system against Palestinians.

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Online continual learning (online CL) studies the problem of learning sequential tasks from an online data stream without task boundaries, aiming to adapt to new data while alleviating catastrophic forgetting on the past tasks. This paper proposes a framework Contrastive Vision Transformer (CVT), which designs a focal contrastive learning strategy based on a transformer architecture, to achieve a better stability-plasticity trade-off for online CL. Specifically, we design a new external attention mechanism for online CL that implicitly captures previous tasks' information. Besides, CVT contains learnable focuses for each class, which could accumulate the knowledge of previous classes to alleviate forgetting. Based on the learnable focuses, we design a focal contrastive loss to rebalance contrastive learning between new and past classes and consolidate previously learned representations. Moreover, CVT contains a dual-classifier structure for decoupling learning current classes and balancing all observed classes. The extensive experimental results show that our approach achieves state-of-the-art performance with even fewer parameters on online CL benchmarks and effectively alleviates the catastrophic forgetting.