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1. ISD: Self-Supervised Learning by Iterative Similarity Distillation

   Tejankar, Ajinkya; Abbasi Koohpayegani, Soroush; Pillai, Vipin; Favaro, Paolo; Pirsiavash, Hamed (October 2021, International Conference on Computer Vision (ICCV) 2021 1 2020)

   Recently, contrastive learning has achieved great results in self-supervised learning, where the main idea is to pull two augmentations of an image (positive pairs) closer compared to other random images (negative pairs). We argue that not all negative images are equally negative. Hence, we introduce a self-supervised learning algorithm where we use a soft similarity for the negative images rather than a binary distinction between positive and negative pairs. We iteratively distill a slowly evolving teacher model to the student model by capturing the similarity of a query image to some random images and transferring that knowledge to the student. Specifically, our method should handle unbalanced and unlabeled data better than existing contrastive learning methods, because the randomly chosen negative set might include many samples that are semantically similar to the query image. In this case, our method labels them as highly similar while standard contrastive methods label them as negatives. Our method achieves comparable results to the state-of-the-art models. 

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2. Slimmed Asymmetrical Contrastive Learning and Cross Distillation for Lightweight Model Training

   Meng, Jian; Yang, Li; Lee, Kyungmin; Shin, Jinwoo; Fan, Deliang; Seo, Jae-sun (December 2023, Thirty-seventh Conference on Neural Information Processing Systems)

   Contrastive learning (CL) has been widely investigated with various learning mech- anisms and achieves strong capability in learning representations of data in a self-supervised manner using unlabeled data. A common fashion of contrastive learning on this line is employing large-sized encoders to achieve comparable performance as the supervised learning counterpart. Despite the success of the labelless training, current contrastive learning algorithms failed to achieve good performance with lightweight (compact) models, e.g., MobileNet, while the re- quirements of the heavy encoders impede the energy-efficient computation, espe- cially for resource-constrained AI applications. Motivated by this, we propose a new self-supervised CL scheme, named SACL-XD, consisting of two technical components, Slimmed Asymmetrical Contrastive Learning (SACL) and Cross- Distillation (XD), which collectively enable efficient CL with compact models. While relevant prior works employed a strong pre-trained model as the teacher of unsupervised knowledge distillation to a lightweight encoder, our proposed method trains CL models from scratch and outperforms them even without such an expensive requirement. Compared to the SoTA lightweight CL training (dis- tillation) algorithms, SACL-XD achieves 1.79% ImageNet-1K accuracy improve- ment on MobileNet-V3 with 64⇥ training FLOPs reduction. Code is available at https://github.com/mengjian0502/SACL-XD. 

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3. Mean Shift for Self-Supervised Learning

   Soroush Abbasi Koohpayegani; Ajinkya Tejankar; Hamed Pirsiavash (October 2021, International Conference on Computer Vision (ICCV))

   Most recent self-supervised learning (SSL) algorithms learn features by contrasting between instances of images or by clustering the images and then contrasting between the image clusters. We introduce a simple mean-shift algorithm that learns representations by grouping images together without contrasting between them or adopting much prior on the structure or number of the clusters. We simply “shift” the embedding of each image to be close to the “mean” of the neighbors of its augmentation. Since the closest neighbor is always another augmentation of the same image, our model will be identical to BYOL when using only one nearest neighbor instead of 5 used in our experiments. Our model achieves 72.4% on ImageNet linear evaluation with ResNet50 at 200 epochs outperforming BYOL. Also, our method outperforms the SOTA by a large margin when using weak augmentations only, facilitating adoption of SSL for other modalities. 

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4. SQUEEZENET: ALEXNET-LEVEL ACCURACY WITH 50X FEWER PARAMETERS AND <0.5MB MODEL SIZE

   Iandola, Forrest N; Han, Song; Moskewicz, MW; Ashraf, K; Dally, WJ; Keutzer, Kurt (February 2016, arXiv.org)

   Recent research on deep neural networks has focused primarily on improving accuracy. For a given accuracy level, it is typically possible to identify multiple DNN architectures that achieve that accuracy level. With equivalent accuracy, smaller DNN architectures offer at least three advantages: (1) Smaller DNNs require less communication across servers during distributed training. (2) Smaller DNNs require less bandwidth to export a new model from the cloud to an autonomous car. (3) Smaller DNNs are more feasible to deploy on FPGAs and other hardware with limited memory. To provide all of these advantages, we propose a small DNN architecture called SqueezeNet. SqueezeNet achieves AlexNet-level accuracy on ImageNet with 50x fewer parameters. Additionally, with model compression techniques we are able to compress SqueezeNet to less than 0.5MB (510x smaller than AlexNet). 

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5. A Simple and Efficient Baseline for Data Attribution on Images

   Singla, Vasu; Sandoval-Segura, Pedro; Goldblum, Micah; Geiping, Jonas; Goldstein, Tom (December 2023, NeurIPS 2023 Workshop ATTRIB)

   Data attribution methods play a crucial role in understanding machine learning models, providing insight into which training data points are most responsible for model outputs during deployment. However, current state-of-the-art approaches require a large ensemble of as many as 300,000 models to accurately attribute model predictions. These approaches therefore come at a high computational cost, are memory intensive, and are hard to scale to large models or datasets. In this work, we focus on a minimalist baseline, utilizing the feature space of a backbone pretrained via self-supervised learning to perform data attribution. Our method is model-agnostic and scales easily to large datasets. We show results on CIFAR-10 and ImageNet, achieving strong performance that rivals or outperforms state-of-the-art approaches at a fraction of the compute or memory cost. Contrary to prior work, our results reinforce the intuition that a model's prediction on one image is most impacted by visually similar training samples. Our approach serves as a simple and efficient baseline for data attribution on images. 

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