PR-231: A Simple Framework for Contrastive Learning of Visual Representations
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The document presents SimCLR, a framework for contrastive learning of visual representations using simple data augmentation. Key aspects of SimCLR include using random cropping and color distortions to generate positive sample pairs for the contrastive loss, a nonlinear projection head to learn representations, and large batch sizes. Evaluation shows SimCLR learns representations that outperform supervised pretraining on downstream tasks and achieves state-of-the-art results with only view augmentation and contrastive loss.
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PR-366: A ConvNet for 2020s
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PR-355: Masked Autoencoders Are Scalable Vision Learners
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PR-344: A Battle of Network Structures: An Empirical Study of CNN, Transforme...
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PR-317: MLP-Mixer: An all-MLP Architecture for Vision
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PR-297: Training data-efficient image transformers & distillation through att...
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PR-284: End-to-End Object Detection with Transformers(DETR)
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PR-270: PP-YOLO: An Effective and Efficient Implementation of Object Detector
PR-270: PP-YOLO: An Effective and Efficient Implementation of Object DetectorJinwon Lee The document presents 'pp-yolo', an object detection model that focuses on balancing effectiveness and efficiency without introducing a novel detector. It details various optimizations, including using a modified backbone network, advanced loss functions, and efficient training methods to improve detection performance while reducing computation time. The findings indicate that pp-yolo achieves a favorable balance of speed and accuracy compared to other state-of-the-art detectors.
PR-258: From ImageNet to Image Classification: Contextualizing Progress on Be...
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PR243: Designing Network Design Spaces
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PR-217: EfficientDet: Scalable and Efficient Object Detection
PR-217: EfficientDet: Scalable and Efficient Object DetectionJinwon Lee EfficientDet presents a scalable object detection architecture aimed at balancing accuracy and efficiency across various resource constraints. It introduces a weighted bidirectional feature network (BiFPN) for improved multi-scale feature fusion and a new compound scaling method that optimizes backbone, feature network, and prediction networks. EfficientDet achieves state-of-the-art accuracy with significantly fewer parameters and computational resources compared to previous models.
PR-207: YOLOv3: An Incremental Improvement
PR-207: YOLOv3: An Incremental ImprovementJinwon Lee YOLOv3 makes the following incremental improvements over previous versions of YOLO:
1. It predicts bounding boxes at three different scales to detect objects more accurately at a variety of sizes.
2. It uses Darknet-53 as its feature extractor, which provides better performance than ResNet while being faster to evaluate.
3. It predicts more bounding boxes overall (over 10,000) to detect objects more precisely, as compared to YOLOv2 which predicts around 800 boxes.
PR-197: One ticket to win them all: generalizing lottery ticket initializatio...
PR-197: One ticket to win them all: generalizing lottery ticket initializatio...Jinwon Lee The document discusses the Lottery Ticket Hypothesis, which suggests that neural networks can be pruned significantly without losing performance, and explores the generalizability of winning ticket initializations across different datasets and optimizers. It demonstrates that winning tickets generated in one configuration can often perform well when transferred to another, but highlights limitations such as the computational expense and uncertainties about the properties of these tickets. The authors propose methods like iterative pruning and late resetting for improved ticket generation and emphasize the importance of dataset characteristics in the success of transferring winning tickets.
PR-183: MixNet: Mixed Depthwise Convolutional Kernels
PR-183: MixNet: Mixed Depthwise Convolutional KernelsJinwon Lee This document reviews the work on MixNet, which proposes mixed depthwise convolutional kernels to improve both accuracy and efficiency in convolutional neural networks (CNNs). It discusses the limitations of traditional depthwise convolutions with a single kernel size and introduces MDConv, a method that combines multiple kernel sizes into a single operation. The results show that MixNet, leveraging neural architecture search techniques, outperforms existing mobile convolutional networks in terms of accuracy and efficiency.
PR-169: EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
PR-169: EfficientNet: Rethinking Model Scaling for Convolutional Neural NetworksJinwon Lee The document presents EfficientNet, a model scaling method for convolutional neural networks, aiming to improve both accuracy and efficiency through a compound scaling approach that balances network width, depth, and resolution. The authors demonstrate that traditional scaling methods often lead to sub-optimal results, while their proposed method simplifies the scaling process by fixing layer architecture and uniformly scaling dimensions with constant ratios. Empirical studies show that this approach significantly enhances performance while operating within resource constraints.
PR-155: Exploring Randomly Wired Neural Networks for Image Recognition
PR-155: Exploring Randomly Wired Neural Networks for Image RecognitionJinwon Lee The document discusses exploring randomly wired neural networks for image recognition. The authors define network generators based on random graph models like Erdos-Renyi, Barabasi-Albert, and Watts-Strogatz. These generators produce neural networks with random connectivity patterns. Experiments show that networks generated this way achieve competitive accuracy compared to hand-designed architectures, demonstrating that the generator design is important. The random wiring patterns provide performance comparable to networks from neural architecture search with fewer parameters and computations.
PR-144: SqueezeNext: Hardware-Aware Neural Network Design
PR-144: SqueezeNext: Hardware-Aware Neural Network DesignJinwon Lee The document discusses SqueezeNext, a hardware-aware neural network designed to improve efficiency and accuracy in deep learning models. It highlights strategies for reducing parameters while enhancing classification performance, particularly for edge devices, by employing techniques such as separable convolutions and bottleneck layers. The architecture shows significant performance improvements over traditional models like AlexNet while maintaining a smaller model size.
PR-132: SSD: Single Shot MultiBox Detector
PR-132: SSD: Single Shot MultiBox DetectorJinwon Lee SSD is a single-shot object detector that processes the entire image at once, rather than proposing regions of interest. It uses a base VGG16 network with additional convolutional layers to predict bounding boxes and class probabilities at three scales simultaneously. SSD achieves state-of-the-art accuracy while running significantly faster than two-stage detectors like Faster R-CNN. It introduces techniques like default boxes, hard negative mining, and data augmentation to address class imbalance and improve results on small objects. On PASCAL VOC 2007, SSD detects objects at 59 FPS with 74.3% mAP, comparable to Faster R-CNN but much faster.
PR-120: ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture De...
PR-120: ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture De...Jinwon Lee The document discusses ShuffleNetV2, a convolutional neural network (CNN) architecture designed for efficiency in mobile devices, emphasizing the importance of metrics such as memory access cost over traditional FLOPs. It outlines practical guidelines for CNN design, including the use of balanced convolutions, cautious application of group convolutions, and reducing fragmentation and element-wise operations. The findings indicate that ShuffleNetV2 achieves improved accuracy and speed compared to its predecessors while maintaining computational efficiency.
PR-108: MobileNetV2: Inverted Residuals and Linear Bottlenecks
PR-108: MobileNetV2: Inverted Residuals and Linear BottlenecksJinwon Lee The paper 'MobileNetV2: Inverted Residuals and Linear Bottlenecks' discusses advancements in mobile neural networks, specifically emphasizing depthwise separable convolutions, linear bottlenecks, and inverted residuals to optimize computational efficiency. It introduces a novel architecture that maintains performance while significantly reducing computational costs, enhancing memory efficiency for mobile and embedded applications. The architecture includes 19 residual bottleneck layers following an initial fully convolutional layer, allowing for effective object detection and semantic segmentation.
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- 1. A Simple Framework for Contrastive Learning ofVisual Representations Ting Chen, et al., “A Simple Framework for Contrastive Learning of Visual Representations” 8th March, 2020 PR12 Paper Review JinWon Lee Samsung Electronics
- 2. References • The Illustrated SimCLR Framework https://amitness.com/2020/03/illustrated-simclr/ • Exploring SimCLR: A Simple Framework for Contrastive Learning of Visual Representations https://towardsdatascience.com/exploring-simclr-a-simple-framework-for- contrastive-learning-of-visual-representations-158c30601e7e • SimCLR: Contrastive Learning ofVisual Representations https://medium.com/@nainaakash012/simclr-contrastive-learning-of-visual- representations-52ecf1ac11fa
- 3. Introduction • Learning effective visual representations without human supervision is a long-standing problem. • Most mainstream approaches fall into one of two classes: generative or discriminative. Generative approaches – pixel level generation is computationally expensive and may not be necessary for representation learning. Discriminative approaches learn representations using objective function like supervised learning but pretext tasks have relied on somewhat ad-hoc heuristics, which limits the generality of learn representations.
- 4. Contrastive Learning – Intuition
- 5. Contrastive Learning • Contrastive methods aim to learn representations by enforcing similar elements to be equal and dissimilar elements to be different.
- 6. Contrastive Learning – Data • Example pairs of images which are similar and images which are different are required for training a model Images from “The Illustrated SimCLR Framework”
- 7. Supervised & Self-supervisedApproach Images from “The Illustrated SimCLR Framework”
- 8. Contrastive Learning – Representstions Images from “The Illustrated SimCLR Framework”
- 9. Contrastive Learning – Similarity Metric Images from “The Illustrated SimCLR Framework”
- 10. Contrastive Learning – Noise Contrastive Estimator Loss • x+ is a positive example and x- is a negative example • sim(.) is a similarity function • Note that each positive pair (x,x+) we have a set of K negatives
- 11. SimCLR
- 12. SimCLR – Overview • A stochastic data augmentation module that transforms any given data example randomly resulting in two correlated views of the same example. Random crop and resize(with random flip), color distortions, and Gaussian blur • ResNet50 is adopted as a encoder, and the output vector is from GAP layer. (2048-dimension) • Two layer MLP is used in projection head. (128-dimensional latent space) • No explicit negative sampling. 2(N-1) augmented examples within a minibatch are used for negative samples. (N is a batch size) • Cosine similarity function is a used similarity metric. • Normalized temperature-scaled cross entropy(NT-Xent) loss is used.
- 13. SimCLR - Overview • Training Batch size : 256~8192 A batch size of 8192 gives 16382 negative examples per positive pair from both augmentation views. To stabilize the training, LARS optimizer is used. Aggregating BN mean and variance over all devices during training. With 128TPU v3 cores, it takes ~1.5 hours to train ResNet-50 with a batch size of 4096 for 100 epochs • Dataset – ImageNet 2012 dataset • To evaluate the learned representations, linear evaluation protocol is used.
- 14. Step by Step Example Images from “The Illustrated SimCLR Framework”
- 15. Step by Step Example Images from “The Illustrated SimCLR Framework”
- 16. Step by Step Example Images from “The Illustrated SimCLR Framework”
- 17. Step by Step Example Images from “The Illustrated SimCLR Framework”
- 18. Step by Step Example Images from “The Illustrated SimCLR Framework”
- 19. Step by Step Example Images from “The Illustrated SimCLR Framework”
- 20. Step by Step Example Images from “The Illustrated SimCLR Framework”
- 21. Step by Step Example Images from “The Illustrated SimCLR Framework”
- 22. Step by Step Example Images from “The Illustrated SimCLR Framework”
- 23. Step by Step Example Images from “The Illustrated SimCLR Framework”
- 24. Data Augmentation for Contrastive Representation Learning • Many existing approaches define contrastive prediction tasks by changing architecture. • The authors use only simple data augmentation methods, this simple design choice conveniently decouples the predictive task from other components such as the NN architecture.
- 25. Data Augmentation for Contrastive Representation Learning Augmentations in the red boxes are used
- 26. Linear Evaluation under Individual or Composition of Data Augmentation
- 27. Evaluation of Data Augmentation • Asymmetric data transformation method is used. Only one branch of the frame work is applied the target transformation(s). • No single transformation suffices to learn good representations, even though the model can almost perfectly identify the positive pairs. • Random cropping and random color distortion stands out When using only random cropping as data augmentation is that most patches from an image share a similar color distortion
- 28. Contrastive Learning Needs Stronger Data Augmentation • Stronger color augmentation substantially improves the linear evaluation of the learned unsupervised models. • A sophisticated augmentation policy(such as AutoAugment) does not work better than simple cropping + (stronger) color distortion • Unsupervised contrastive learning benefits from stronger (color) data augmentation than supervised learning. • Data augmentation that does not yield accuracy benefits for supervised learning can still help considerably with contrastive learning.
- 29. Unsupervised Contrastive Learning Benefits from Bigger Models • Unsupervised learning benefits more from bigger models than its supervised counter part.
- 30. Nonlinear Projection Head • Nonlinear projection is better than a linear projection(+3%) and much better than no projection(>10%)
- 31. Nonlinear Projection Head • The hidden layer before the projection head is a better representation than the layer after. • The importance of using the representation before the nonlinear projection is due to loss of information induced by the contrastive loss. In particular z = g(h) is trained to be invariant to data transformation.
- 32. Loss Function • l2 normalization along with temperature effectively weights different examples, and an appropriate temperature can help the model learn from hard negatives. • Unlike cross-entropy, other objective functions do not weigh the negatives by their relative hardness.
- 33. Larger Batch Size and LongerTraining • When the training epochs is small, larger batch size have a significant advantage. • Larger batch sizes provide more negative examples, facilitating convergence, and training longer also provides more negative examples, improving the results.
- 34. Comparison with SOTA – Linear Evaluation
- 35. Comparison with SOTA – Semi-supervised Learning
- 38. Appendix – Effects of LongerTraining for Supervised Learning • There is no significant benefit from training supervised models longer on ImageNet. • Stronger data augmentation slightly improves the accuracy of ResNet-50 (4x) but does not help on ResNet-50.
- 39. Appendix – CIFAR-10 Dataset Results
- 40. Conclusion • SimCLR differs from standard supervised learning on ImageNet only in the choice of data augmentation, the use of nonlinear head at the end of the network, and the loss function. • Composition of data augmentations plays a critical role in defining effective predictive tasks. • Nonlinear transformation between the representation and the contrastive loss substantially improves the quality of the learned representations. • Contrastive learning benefits from larger batch sizes and more training steps compared to supervised learning. • SimCLR achieves 76.5% top-1 accuracy, which is a 7% relative improvement over previous state-of-the-art, matching the performance of a supervised ResNet-50.