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+<h1 style="text-align:center"> Batch Model Consolidation: A Multi-Task Model Consolidation Framework </h1>
+
+<h3 style="text-align:center"> Iordanis Fostiropoulos &nbsp;&nbsp;&nbsp; Jiaye Zhu &nbsp;&nbsp;&nbsp; Laurent Itti</h3>
+<p style="text-align:center"> University of Southern California</p>
+
+<p style="text-align:center"> 
+<a href="">[Arxiv]</a> 
+&nbsp;&nbsp;&nbsp; 
+<a href="https://github.com/fostiropoulos/stream_benchmark">[Code]</a>  
+&nbsp;&nbsp;&nbsp; 
+<a href="https://github.com/fostiropoulos/stream">[Dataset]</a> 
+</p>
+
+## Abstract
+
+In Continual Learning (CL), a model is required to learn a stream of tasks sequentially 
+without significant performance degradation on previously learned tasks. 
+Current approaches fail for a long sequence of tasks from diverse domains and difficulties. 
+Many of the existing CL approaches are difficult to apply in practice due to excessive memory 
+cost or training time, or are tightly coupled to a single device. With the intuition 
+derived from the widely applied mini-batch training, we propose Batch Model Consolidation 
+(**BMC**) to support more realistic CL under conditions where multiple agents are 
+exposed to a range of tasks. During a _regularization_ phase, BMC trains multiple 
+_expert models_ in parallel on a set of disjoint tasks. Each expert maintains weight 
+similarity to a _base model_ through a _stability loss_, and constructs a 
+_buffer_ from a fraction of the task's data. During the _consolidation_ phase, 
+combine the learned knowledge on `batches' of _expert models_ using a 
+_batched consolidation loss_ in _memory_ data that aggregates all buffers. 
+We thoroughly evaluate each component of our method in an ablation study and demonstrate 
+the effectiveness on standardized benchmark datasets Split-CIFAR-100, Tiny-ImageNet, 
+and the Stream dataset composed of 71 image classification tasks from diverse domains 
+and difficulties. Our method outperforms the next best CL approach by 70% and is the 
+only approach that can maintain performance at the end of 71 tasks.
+
+## Overview
+
+Intuition: similar to mini-batch training, batched task training can reduce the local minima and improve the convexity of the loss landscape.
+
+<p style="text-align:center">
+<img src="https://drive.google.com/uc?export=view&id=1ZgwGy1Ta2u9Wim0D010uf7cSGw07qts9" alt="drawing" width="60%"/>
+</p>
+
+BMC optimizes multiple expert models from a single base model in parallel on **different** tasks,
+enforcing parameter-isolation. Experts are regularized during training to reduce the forgetting 
+on tasks learned by base model. A new base model is consolidated by **batched distillation** from the experts.
+
+![A single incremental step of BMC](https://drive.google.com/uc?export=view&id=1nG4kD2PCP0sMZxBRD3LN8fZjzYvQrpTJ)
+
+BMC supports distributed training where experts are trained locally on remote devices. 
+Artifacts are sent back to the central device for consolidation training. 
+The parallelism of this framework enables BMC to learn long task sequences efficiently.
+
+![Paralleled multi-expert training framework](https://drive.google.com/uc?export=view&id=1NAswFVQtiNn6xkilUig42guGfvi-babV)
+
+## The Stream Dataset
+
+Stream dataset implements the logic for processing and managing a large sequence of datasets, 
+and provides a method to train on interdisciplinary tasks by projecting all datasets on the same dimension,
+by extracting features from pre-trained models.
+
+See [the repository](https://github.com/fostiropoulos/stream/tree/cvpr_release) for Stream dataset installation and usages.
+
+## Class-Incremental Learning
+
+We show on the Stream dataset with CLIP embedding that our method outperforms all other baselines in the Class-Incremental Learning scenario.
+Our implementation of BMC as well as the baselines can be found [here](https://github.com/fostiropoulos/stream_benchmark).
+
+![Experiment result on Stream](https://drive.google.com/uc?export=view&id=1rNjwxvOUYDcSOof9HTrD3eB0l0w_yM-8)
+
+
+
+## Citation
+
+```
+
+```

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