The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models

The computer vision world has been re-gaining enthusiasm in various pre-trained models, including both classical ImageNet supervised pre-training and recently emerged self-supervised pre-training such as simCLR and MoCo. Pre-trained weights often boost a wide range of downstream tasks including classification, detection, and segmentation. Latest studies suggest that pre-training benefits from gigantic model capacity. We are hereby curious and ask: after pre-training, does a pre-trained model indeed have to stay large for its downstream transferability? In this paper, we examine supervised and self-supervised pre-trained models through the lens of the lottery ticket hypothesis (LTH). LTH identifies highly sparse matching subnetworks that can be trained in isolation from (nearly) scratch yet still reach the full models' performance. We extend the scope of LTH and question whether matching subnetworks still exist in pre-trained computer vision models, that enjoy the same downstream transfer performance. Our extensive experiments convey an overall positive message: from all pre-trained weights obtained by ImageNet classification, simCLR, and MoCo, we are consistently able to locate such matching subnetworks at 59.04% to 96.48% sparsity that transfer universally to multiple downstream tasks, whose performance see no degradation compared to using full pre-trained weights. Further analyses reveal that subnetworks found from different pre-training tend to yield diverse mask structures and perturbation sensitivities. We conclude that the core LTH observations remain generally relevant in the pre-training paradigm of computer vision, but more delicate discussions are needed in some cases. Codes and pre-trained models will be made available at: https://github.com/VITA-Group/CV_LTH_Pre-training.Comment: CVPR 202

COLT: Cyclic Overlapping Lottery Tickets for Faster Pruning of Convolutional Neural Networks

Pruning refers to the elimination of trivial weights from neural networks. The sub-networks within an overparameterized model produced after pruning are often called Lottery tickets. This research aims to generate winning lottery tickets from a set of lottery tickets that can achieve similar accuracy to the original unpruned network. We introduce a novel winning ticket called Cyclic Overlapping Lottery Ticket (COLT) by data splitting and cyclic retraining of the pruned network from scratch. We apply a cyclic pruning algorithm that keeps only the overlapping weights of different pruned models trained on different data segments. Our results demonstrate that COLT can achieve similar accuracies (obtained by the unpruned model) while maintaining high sparsities. We show that the accuracy of COLT is on par with the winning tickets of Lottery Ticket Hypothesis (LTH) and, at times, is better. Moreover, COLTs can be generated using fewer iterations than tickets generated by the popular Iterative Magnitude Pruning (IMP) method. In addition, we also notice COLTs generated on large datasets can be transferred to small ones without compromising performance, demonstrating its generalizing capability. We conduct all our experiments on Cifar-10, Cifar-100 & TinyImageNet datasets and report superior performance than the state-of-the-art methods

Pruning Convolutional Neural Networks with Self-Supervision

Convolutional neural networks trained without supervision come close to matching performance with supervised pre-training, but sometimes at the cost of an even higher number of parameters. Extracting subnetworks from these large unsupervised convnets with preserved performance is of particular interest to make them less computationally intensive. Typical pruning methods operate during training on a task while trying to maintain the performance of the pruned network on the same task. However, in self-supervised feature learning, the training objective is agnostic on the representation transferability to downstream tasks. Thus, preserving performance for this objective does not ensure that the pruned subnetwork remains effective for solving downstream tasks. In this work, we investigate the use of standard pruning methods, developed primarily for supervised learning, for networks trained without labels (i.e. on self-supervised tasks). We show that pruned masks obtained with or without labels reach comparable performance when re-trained on labels, suggesting that pruning operates similarly for self-supervised and supervised learning. Interestingly, we also find that pruning preserves the transfer performance of self-supervised subnetwork representations

Robust Tickets Can Transfer Better: Drawing More Transferable Subnetworks in Transfer Learning

Transfer learning leverages feature representations of deep neural networks (DNNs) pretrained on source tasks with rich data to empower effective finetuning on downstream tasks. However, the pretrained models are often prohibitively large for delivering generalizable representations, which limits their deployment on edge devices with constrained resources. To close this gap, we propose a new transfer learning pipeline, which leverages our finding that robust tickets can transfer better, i.e., subnetworks drawn with properly induced adversarial robustness can win better transferability over vanilla lottery ticket subnetworks. Extensive experiments and ablation studies validate that our proposed transfer learning pipeline can achieve enhanced accuracy-sparsity trade-offs across both diverse downstream tasks and sparsity patterns, further enriching the lottery ticket hypothesis.Comment: Accepted by DAC 202

HUMAN ACTIVITY RECOGNITION FROM EGOCENTRIC VIDEOS AND ROBUSTNESS ANALYSIS OF DEEP NEURAL NETWORKS

In recent years, there has been significant amount of research work on human activity classification relying either on Inertial Measurement Unit (IMU) data or data from static cameras providing a third-person view. There has been relatively less work using wearable cameras, providing egocentric view, which is a first-person view providing the view of the environment as seen by the wearer. Using only IMU data limits the variety and complexity of the activities that can be detected. Deep machine learning has achieved great success in image and video processing in recent years. Neural network based models provide improved accuracy in multiple fields in computer vision. However, there has been relatively less work focusing on designing specific models to improve the performance of egocentric image/video tasks. As deep neural networks keep improving the accuracy in computer vision tasks, the robustness and resilience of the networks should be improved as well to make it possible to be applied in safety-crucial areas such as autonomous driving. Motivated by these considerations, in the first part of the thesis, the problem of human activity detection and classification from egocentric cameras is addressed. First, anew method is presented to count the number of footsteps and compute the total traveled distance by using the data from the IMU sensors and camera of a smart phone. By incorporating data from multiple sensor modalities, and calculating the length of each step, instead of using preset stride lengths and assuming equal-length steps, the proposed method provides much higher accuracy compared to commercially available step counting apps. After the application of footstep counting, more complicated human activities, such as steps of preparing a recipe and sitting on a sofa, are taken into consideration. Multiple classification methods, non-deep learning and deep-learning-based, are presented, which employ both ego-centric camera and IMU data. Then, a Genetic Algorithm-based approach is employed to set the parameters of an activity classification network autonomously and performance is compared with empirically-set parameters. Then, a new framework is introduced to reduce the computational cost of human temporal activity recognition from egocentric videos while maintaining the accuracy at a comparable level. The actor-critic model of reinforcement learning is applied to optical flow data to locate a bounding box around region of interest, which is then used for clipping a sub-image from a video frame. A shallow and deeper 3D convolutional neural network is designed to process the original image and the clipped image region, respectively.Next, a systematic method is introduced that autonomously and simultaneously optimizes multiple parameters of any deep neural network by using a bi-generative adversarial network (Bi-GAN) guiding a genetic algorithm(GA). The proposed Bi-GAN allows the autonomous exploitation and choice of the number of neurons for the fully-connected layers, and number of filters for the convolutional layers, from a large range of values. The Bi-GAN involves two generators, and two different models compete and improve each other progressively with a GAN-based strategy to optimize the networks during a GA evolution.In this analysis, three different neural network layers and datasets are taken into consideration: First, 3D convolutional layers for ModelNet40 dataset. We applied the proposed approach on a 3D convolutional network by using the ModelNet40 dataset. ModelNet is a dataset of 3D point clouds. The goal is to perform shape classification over 40shape classes. LSTM layers for UCI HAR dataset. UCI HAR dataset is composed of InertialMeasurement Unit (IMU) data captured during activities of standing, sitting, laying, walking, walking upstairs and walking downstairs. These activities were performed by 30 subjects, and the 3-axial linear acceleration and 3-axial angular velocity were collected at a constant rate of 50Hz. 2D convolutional layers for Chars74k Dataset. Chars74k dataset contains 64 classes(0-9, A-Z, a-z), 7705 characters obtained from natural images, 3410 hand-drawn characters using a tablet PC and 62992 synthesised characters from computer fonts giving a total of over 74K images. In the final part of the thesis, network robustness and resilience for neural network models is investigated from adversarial examples (AEs) and automatic driving conditions. The transferability of adversarial examples across a wide range of real-world computer vision tasks, including image classification, explicit content detection, optical character recognition(OCR), and object detection are investigated. It represents the cybercriminal’s situation where an ensemble of different detection mechanisms need to be evaded all at once.Novel dispersion Reduction(DR) attack is designed, which is a practical attack that overcomes existing attacks’ limitation of requiring task-specific loss functions by targeting on the “dispersion” of internal feature map. In the autonomous driving scenario, the adversarial machine learning attacks against the complete visual perception pipeline in autonomous driving is studied. A novel attack technique, tracker hijacking, that can effectively fool Multi-Object Tracking (MOT) using AEs on object detection is presented. Using this technique, successful AEs on as few as one single frame can move an existing object in to or out of the headway of an autonomous vehicle to cause potential safety hazards

Why is Machine Learning Security so hard?

The increase of available data and computing power has fueled a wide application of machine learning (ML). At the same time, security concerns are raised: ML models were shown to be easily fooled by slight perturbations on their inputs. Furthermore, by querying a model and analyzing output and input pairs, an attacker can infer the training data or replicate the model, thereby harming the owner’s intellectual property. Also, altering the training data can lure the model into producing specific or generally wrong outputs at test time. So far, none of the attacks studied in the field has been satisfactorily defended. In this work, we shed light on these difficulties. We first consider classifier evasion or adversarial examples. The computation of such examples is an inherent problem, as opposed to a bug that can be fixed. We also show that adversarial examples often transfer from one model to another, different model. Afterwards, we point out that the detection of backdoors (a training-time attack) is hindered as natural backdoor-like patterns occur even in benign neural networks. The question whether a pattern is benign or malicious then turns into a question of intention, which is hard to tackle. A different kind of complexity is added with the large libraries nowadays in use to implement machine learning. We introduce an attack that alters the library, thereby decreasing the accuracy a user can achieve. In case the user is aware of the attack, however, it is straightforward to defeat. This is not the case for most classical attacks described above. Additional difficulty is added if several attacks are studied at once: we show that even if the model is configured for one attack to be less effective, another attack might perform even better. We conclude by pointing out the necessity of understanding the ML model under attack. On the one hand, as we have seen throughout the examples given here, understanding precedes defenses and attacks. On the other hand, an attack, even a failed one, often yields new insights and knowledge about the algorithm studied.This work was supported by the German Federal Ministry of Education and Research (BMBF) through funding for the Center for IT-Security,Privacy and Accountability (CISPA) (FKZ: 16KIS0753

Reprogramming under constraints: Revisiting efficient and reliable transferability of lottery tickets

In the era of foundation models with huge pre-training budgets, the downstream tasks have been shifted to the narrative of efficient and fast adaptation. For classification-based tasks in the domain of computer vision, the two most efficient approaches have been linear probing (LP) and visual prompting/reprogramming (VP); the former aims to learn a classifier in the form of a linear head on the features extracted by the pre-trained model, while the latter maps the input data to the domain of the source data on which the model was originally pre-trained on. Although extensive studies have demonstrated the differences between LP and VP in terms of downstream performance, we explore the capabilities of the two aforementioned methods via the sparsity axis: (a) Data sparsity: the impact of few-shot adaptation and (b) Model sparsity: the impact of lottery tickets (LT). We demonstrate that LT are not universal reprogrammers, i.e., for certain target datasets, reprogramming an LT yields significantly lower performance than the reprogrammed dense model although their corresponding upstream performance is similar. Further, we demonstrate that the calibration of dense models is always superior to that of their lottery ticket counterparts under both LP and VP regimes. Our empirical study opens a new avenue of research into VP for sparse models and encourages further understanding of the performance beyond the accuracy achieved by VP under constraints of sparsity. Code and logs can be accessed at \url{https://github.com/landskape-ai/Reprogram_LT}.Comment: Preprin

Pruning Convolutional Neural Networks with Self-Supervision

Convolutional neural networks trained without supervision come close to matching performance with supervised pre-training, but sometimes at the cost of an even higher number of parameters. Extracting subnetworks from these large unsupervised convnets with preserved performance is of particular interest to make them less computationally intensive. Typical pruning methods operate during training on a task while trying to maintain the performance of the pruned network on the same task. However, in self-supervised feature learning, the training objective is agnostic on the representation transferability to downstream tasks. Thus, preserving performance for this objective does not ensure that the pruned subnetwork remains effective for solving downstream tasks. In this work, we investigate the use of standard pruning methods, developed primarily for supervised learning, for networks trained without labels (i.e. on self-supervised tasks). We show that pruned masks obtained with or without labels reach comparable performance when retrained on labels, suggesting that pruning operates similarly for self-supervised and supervised learning. Interestingly, we also find that pruning preserves the transfer performance of self-supervised subnetwork representations

Towards Compute-Optimal Transfer Learning

The field of transfer learning is undergoing a significant shift with the introduction of large pretrained models which have demonstrated strong adaptability to a variety of downstream tasks. However, the high computational and memory requirements to finetune or use these models can be a hindrance to their widespread use. In this study, we present a solution to this issue by proposing a simple yet effective way to trade computational efficiency for asymptotic performance which we define as the performance a learning algorithm achieves as compute tends to infinity. Specifically, we argue that zero-shot structured pruning of pretrained models allows them to increase compute efficiency with minimal reduction in performance. We evaluate our method on the Nevis'22 continual learning benchmark that offers a diverse set of transfer scenarios. Our results show that pruning convolutional filters of pretrained models can lead to more than 20% performance improvement in low computational regimes

Lottery Tickets in Evolutionary Optimization: On Sparse Backpropagation-Free Trainability

Is the lottery ticket phenomenon an idiosyncrasy of gradient-based training or does it generalize to evolutionary optimization? In this paper we establish the existence of highly sparse trainable initializations for evolution strategies (ES) and characterize qualitative differences compared to gradient descent (GD)-based sparse training. We introduce a novel signal-to-noise iterative pruning procedure, which incorporates loss curvature information into the network pruning step. This can enable the discovery of even sparser trainable network initializations when using black-box evolution as compared to GD-based optimization. Furthermore, we find that these initializations encode an inductive bias, which transfers across different ES, related tasks and even to GD-based training. Finally, we compare the local optima resulting from the different optimization paradigms and sparsity levels. In contrast to GD, ES explore diverse and flat local optima and do not preserve linear mode connectivity across sparsity levels and independent runs. The results highlight qualitative differences between evolution and gradient-based learning dynamics, which can be uncovered by the study of iterative pruning procedures.Comment: 13 pages, 11 figures, International Conference on Machine Learning (ICML) 202