Eugene Belilovsky

pietro.cagnasso@mila.quebec

Xiao Huang

Master's Research - Concordia University

Co-supervisor :

xiaolong.huang@mila.quebec

Paul Janson

PhD - Concordia University

Co-supervisor :

paul.janson@mila.quebec

Gwen Legate

PhD - Concordia University

Co-supervisor :

gwendolyne.legate@mila.quebec

Paria Mehrbod

Master's Research - Concordia University

Co-supervisor :

paria.mehrbod@mila.quebec

Abhinav Moudgil

PhD - Concordia University

abhinav.moudgil@mila.quebec

Adel Nabli

PhD - Concordia University

adel.nabli@mila.quebec

Geraldin Nanfack

Postdoctorate - Concordia University

Co-supervisor :

geraldin.nanfack@mila.quebec

Albert Orozco Camacho

PhD - Concordia University

Co-supervisor :

PhD - Concordia University

Co-supervisor :

vaibhav.singh@mila.quebec

Benjamin Therien

PhD - Université de Montréal

Principal supervisor :

benjamin.therien@mila.quebec

Research Intern - Concordia University

junior-cedric.tonga@mila.quebec

Zhang Yanlei

Collaborating Alumni - Université de Montréal

Principal supervisor :

yanlei.zhang@mila.quebec

amirhossein.zamani@mila.quebec

Amirhossein Zamani

PhD - Concordia University

Co-supervisor :

Blake Richards

Publications

Can Forward Gradient Match Backpropagation?

Louis Fournier

Stéphane Rivaud

Michael Eickenberg

Edouard Oyallon

Forward Gradients - the idea of using directional derivatives in forward differentiation mode - have recently been shown to be utilizable fo… (see more)r neural network training while avoiding problems generally associated with backpropagation gradient computation, such as locking and memorization requirements. The cost is the requirement to guess the step direction, which is hard in high dimensions. While current solutions rely on weighted averages over isotropic guess vector distributions, we propose to strongly bias our gradient guesses in directions that are much more promising, such as feedback obtained from small, local auxiliary networks. For a standard computer vision neural network, we conduct a rigorous study systematically covering a variety of combinations of gradient targets and gradient guesses, including those previously presented in the literature. We find that using gradients obtained from a local loss as a candidate direction drastically improves on random noise in Forward Gradient methods.

2022-12-31

ICML (published)

Prototype-Sample Relation Distillation: Towards Replay-Free Continual Learning

Nader Asadi

MohammadReza Davari

Sudhir Mudur

Rahaf Aljundi

In Continual learning (CL) balancing effective adaptation while combating catastrophic forgetting is a central challenge. Many of the recent… (see more) best-performing methods utilize various forms of prior task data, e.g. a replay buffer, to tackle the catastrophic forgetting problem. Having access to previous task data can be restrictive in many real-world scenarios, for example when task data is sensitive or proprietary. To overcome the necessity of using previous tasks' data, in this work, we start with strong representation learning methods that have been shown to be less prone to forgetting. We propose a holistic approach to jointly learn the representation and class prototypes while maintaining the relevance of old class prototypes and their embedded similarities. Specifically, samples are mapped to an embedding space where the representations are learned using a supervised contrastive loss. Class prototypes are evolved continually in the same latent space, enabling learning and prediction at any point. To continually adapt the prototypes without keeping any prior task data, we propose a novel distillation loss that constrains class prototypes to maintain relative similarities as compared to new task data. This method yields state-of-the-art performance in the task-incremental setting, outperforming methods relying on large amounts of data, and provides strong performance in the class-incremental setting without using any stored data points.

2022-12-31

ICML (published)

Re-Weighted Softmax Cross-Entropy to Control Forgetting in Federated Learning

Gwen Legate

Lucas Caccia

In Federated Learning a global model is learned by aggregating model updates computed at a set of independent client nodes. To reduce commun… (see more)ication costs, multiple gradient steps are performed at each node prior to aggregation. A key challenge in this setting is data heterogeneity across clients resulting in differing local objectives. This can lead clients to overly minimize their own local objective consequently diverging from the global solution. We demonstrate that individual client models experience a catastrophic forgetting with respect to data from other clients and propose an efficient approach that modifies the cross-entropy objective on a per-client basis by re-weighting the softmax logits prior to computing the loss. This approach shields classes outside a client’s label set from abrupt representation change and we empirically demonstrate it can alleviate client forgetting and provide consistent improvements to standard federated learning algorithms. Our method is particularly beneficial under the most challenging federated learning settings where data heterogeneity is high and client participation in each round is low.

2022-12-31

CoLLAs (published)

Imitation from Observation With Bootstrapped Contrastive Learning

Medric Sonwa

Johanna Hansen

2022-12-08

NeurIPS.cc/2022/Workshop/DeepRL (accepted)

openreview.net

CLIP-Mesh: Generating textured meshes from text using pretrained image-text models

Nasir Mohammad Khalid

Tianhao Xie

Tiberiu Popa

We present a technique for zero-shot generation of a 3D model using only a target text prompt. Without any 3D supervision our method deforms… (see more) the control shape of a limit subdivided surface along with its texture map and normal map to obtain a 3D asset that corresponds to the input text prompt and can be easily deployed into games or modeling applications. We rely only on a pre-trained CLIP model that compares the input text prompt with differentiably rendered images of our 3D model. While previous works have focused on stylization or required training of generative models we perform optimization on mesh parameters directly to generate shape, texture or both. To constrain the optimization to produce plausible meshes and textures we introduce a number of techniques using image augmentations and the use of a pretrained prior that generates CLIP image embeddings given a text embedding.

2022-11-29

SIGGRAPH Asia 2022 Conference Papers (published)

Towards Scaling Difference Target Propagation by Learning Backprop Targets

The development of biologically-plausible learning algorithms is important for understanding learning in the brain, but most of them fail to… (see more) scale-up to real-world tasks, limiting their potential as explanations for learning by real brains. As such, it is important to explore learning algorithms that come with strong theoretical guarantees and can match the performance of backpropagation (BP) on complex tasks. One such algorithm is Difference Target Propagation (DTP), a biologically-plausible learning algorithm whose close relation with Gauss-Newton (GN) optimization has been recently established. However, the conditions under which this connection rigorously holds preclude layer-wise training of the feedback pathway synaptic weights (which is more biologically plausible). Moreover, good alignment between DTP weight updates and loss gradients is only loosely guaranteed and under very specific conditions for the architecture being trained. In this paper, we propose a novel feedback weight training scheme that ensures both that DTP approximates BP and that layer-wise feedback weight training can be restored without sacrificing any theoretical guarantees. Our theory is corroborated by experimental results and we report the best performance ever achieved by DTP on CIFAR-10 and ImageNet 32

2022-06-27

Proceedings of the 39th International Conference on Machine Learning (published)

Probing Representation Forgetting in Supervised and Unsupervised Continual Learning

MohammadReza Davari

Nader Asadi

Sudhir Mudur

Rahaf Aljundi

Continual Learning research typically focuses on tackling the phenomenon of catastrophic forgetting in neural networks. Catastrophic forgett… (see more)ing is associated with an abrupt loss of knowledge previously learned by a model when the task, or more broadly the data distribution, being trained on changes. In supervised learning problems this forgetting, resulting from a change in the model's representation, is typically measured or observed by evaluating the decrease in old task performance. However, a model's representation can change without losing knowledge about prior tasks. In this work we consider the concept of representation forgetting, observed by using the difference in performance of an optimal linear classifier before and after a new task is introduced. Using this tool we revisit a number of standard continual learning benchmarks and observe that, through this lens, model representations trained without any explicit control for forgetting often experience small representation forgetting and can sometimes be comparable to methods which explicitly control for forgetting, especially in longer task sequences. We also show that representation forgetting can lead to new insights on the effect of model capacity and loss function used in continual learning. Based on our results, we show that a simple yet competitive approach is to learn representations continually with standard supervised contrastive learning while constructing prototypes of class samples when queried on old samples.

2022-06-17

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (published)

Revisiting Learnable Affines for Batch Norm in Few-Shot Transfer Learning

Moslem Yazdanpanah

Aamer Abdul Rahman

Muawiz Sajjad Chaudhary

Christian Desrosiers

Mohammad Havaei

S Ebrahimi Kahou

Batch normalization is a staple of computer vision models, including those employed in few-shot learning. Batch nor-malization layers in con… (see more)volutional neural networks are composed of a normalization step, followed by a shift and scale of these normalized features applied via the per-channel trainable affine parameters

2022-06-17

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (published)

Parametric Scattering Networks

Shanel Gauthier

Benjamin Therien

Laurent Alsène-Racicot

Michael Eickenberg

The wavelet scattering transform creates geometric invariants and deformation stability. In multiple signal domains, it has been shown to yi… (see more)eld more discriminative representations compared to other non-learned representations and to outperform learned representations in certain tasks, particularly on limited labeled data and highly structured signals. The wavelet filters used in the scattering transform are typically selected to create a tight frame via a parameterized mother wavelet. In this work, we investigate whether this standard wavelet filterbank construction is optimal. Focusing on Morlet wavelets, we propose to learn the scales, orientations, and aspect ratios of the filters to produce problem-specific parameterizations of the scattering transform. We show that our learned versions of the scattering transform yield significant performance gains in small-sample classification settings over the standard scattering transform. Moreover, our empirical results suggest that traditional filterbank constructions may not always be necessary for scattering transforms to extract effective representations.

2022-05-31

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (published)

New Insights on Reducing Abrupt Representation Change in Online Continual Learning

Lucas Caccia

Tinne Tuytelaars

In the online continual learning paradigm, agents must learn from a changing distribution while respecting memory and compute constraints. E… (see more)xperience Replay (ER), where a small subset of past data is stored and replayed alongside new data, has emerged as a simple and effective learning strategy. In this work, we focus on the change in representations of observed data that arises when previously unobserved classes appear in the incoming data stream, and new classes must be distinguished from previous ones. We shed new light on this question by showing that applying ER causes the newly added classes' representations to overlap significantly with the previous classes, leading to highly disruptive parameter updates. Based on this empirical analysis, we propose a new method which mitigates this issue by shielding the learned representations from drastic adaptation to accommodate new classes. We show that using an asymmetric update rule pushes new classes to adapt to the older ones (rather than the reverse), which is more effective especially at task boundaries, where much of the forgetting typically occurs. Empirical results show significant gains over strong baselines on standard continual learning benchmarks

2022-04-24

International Conference on Learning Representations (Accept (Poster))

openreview.net

Local Learning with Neuron Groups

Adeetya Patel

Michael Eickenberg

2022-04-20

ICLR.cc/2022/Workshop/Cells2Societies (poster)

openreview.net

Generative Compositional Augmentations for Scene Graph Prediction

Boris Knyazev

Harm de Vries

Cătălina Cangea

Graham W. Taylor

Aaron Courville

Inferring objects and their relationships from an image in the form of a scene graph is useful in many applications at the intersection of v… (see more)ision and language. We consider a challenging problem of compositional generalization that emerges in this task due to a long tail data distribution. Current scene graph generation models are trained on a tiny fraction of the distribution corresponding to the most frequent compositions, e.g. . However, test images might contain zero- and few-shot compositions of objects and relationships, e.g. . Despite each of the object categories and the predicate (e.g. 'on') being frequent in the training data, the models often fail to properly understand such unseen or rare compositions. To improve generalization, it is natural to attempt increasing the diversity of the training distribution. However, in the graph domain this is non-trivial. To that end, we propose a method to synthesize rare yet plausible scene graphs by perturbing real ones. We then propose and empirically study a model based on conditional generative adversarial networks (GANs) that allows us to generate visual features of perturbed scene graphs and learn from them in a joint fashion. When evaluated on the Visual Genome dataset, our approach yields marginal, but consistent improvements in zero- and few-shot metrics. We analyze the limitations of our approach indicating promising directions for future research.

2021-09-30

2021 IEEE/CVF International Conference on Computer Vision (ICCV) (published)