Nikolaos Giakoumoglou

SynCo: Synthetic Hard Negatives for Contrastive Visual Representation Learning

N. Giakoumoglou and T. Stathaki

[arXiv] [pdf] [suppl] [code] [bibtex] [paperswithcode]

Contrastive learning has become a dominant approach in self-supervised visual representation learning, but efficiently leveraging hard negatives, which are samples closely resembling the anchor, remains challenging. We introduce SynCo (Synthetic negatives in Contrastive learning), a novel approach that improves model performance by generating synthetic hard negatives on the representation space. Building on the MoCo framework, SynCo introduces six strategies for creating diverse synthetic hard negatives on-the-fly with minimal computational overhead. SynCo achieves faster training and strong representation learning, surpassing MoCo-v2 by +0.4% and MoCHI by +1.0% on ImageNet ILSVRC-2012 linear evaluation. It also transfers more effectively to detection tasks achieving strong results on PASCAL VOC detection (57.2% AP) and significantly improving over MoCo-v2 on COCO detection (+1.0% AP) and instance segmentation (+0.8% AP). Our synthetic hard negative generation approach significantly enhances visual representations learned through self-supervised contrastive learning.

Relational Representation Distillation

N. Giakoumoglou and T. Stathaki

[arXiv] [pdf] [suppl] [code] [bibtex]

Knowledge distillation involves transferring knowledge from large, cumbersome teacher models to more compact student models. The standard approach minimizes the Kullback-Leibler (KL) divergence between the probabilistic outputs of a teacher and student network using a shared temperature-based softmax function. However, this approach fails to capture important structural relationships in the teacher's internal representations. Recent advances have turned to contrastive learning objectives, but these methods impose overly strict constraints through instance-discrimination, forcing apart semantically similar samples even when they should maintain similarity. This motivates an alternative objective by which we preserve relative relationships between instances. Our method employs separate temperature parameters for teacher and student distributions, with sharper student outputs, enabling precise learning of primary relationships while preserving secondary similarities. We show theoretical connections between our objective and both InfoNCE loss and KL divergence. Experiments demonstrate that our method significantly outperforms existing knowledge distillation methods across diverse knowledge transfer tasks, and sometimes even outperforms the teacher network.

Discriminative and Consistent Representation Distillation

N. Giakoumoglou and T. Stathaki

[arXiv] [pdf] [suppl] [code] [bibtex]

Knowledge Distillation (KD) aims to transfer knowledge from a large teacher model to a smaller student model. While contrastive learning has shown promise in self-supervised learning by creating discriminative representations, its application in knowledge distillation remains limited and focuses primarily on discrimination, neglecting the structural relationships captured by the teacher model. To address this limitation, we propose Discriminative and Consistent Distillation (DCD), which employs a contrastive loss along with a consistency regularization to minimize the discrepancy between the distributions of teacher and student representations. Our method introduces learnable temperature and bias parameters that adapt during training to balance these complementary objectives, replacing the fixed hyperparameters commonly used in contrastive learning approaches. Through extensive experiments on CIFAR-100 and ImageNet ILSVRC-2012, we demonstrate that DCD achieves state-of-the-art performance, with the student model sometimes surpassing the teacher's accuracy. Furthermore, we show that DCD's learned representations exhibit superior cross-dataset generalization when transferred to Tiny ImageNet and STL-10.

A Multimodal Approach for Cross-Domain Image Retrieval

L. Iijima, N. Giakoumoglou and T. Stathaki

[arXiv] [bibtex]

Cross-Domain Image Retrieval (CDIR) is a challenging task in computer vision, aiming to match images across different visual domains such as sketches, paintings, and photographs. Traditional approaches focus on visual image features and rely heavily on supervised learning with labeled data and cross-domain correspondences, which leads to an often struggle with the significant domain gap. This paper introduces a novel unsupervised approach to CDIR that incorporates textual context by leveraging pre-trained vision-language models. Our method, dubbed as Caption-Matching (CM), uses generated image captions as a domain-agnostic intermediate representation, enabling effective cross-domain similarity computation without the need for labeled data or fine-tuning. We evaluate our method on standard CDIR benchmark datasets, demonstrating state-of-the-art performance in unsupervised settings with improvements of 24.0% on Office-Home and 132.2% on DomainNet over previous methods. We also demonstrate our method’s effectiveness on a dataset of AI-generated images from Midjourney, showcasing its ability to handle complex, multi-domain queries.

A Review on Discriminative Self-supervised Learning Methods

N. Giakoumoglou, A. Gkelias, T. Stathaki

[arXiv]

TBA

Expert Clustering and Knowledge Transfer for Whole Slide Image Classification

K. M. Papadopoulos, N. Giakoumoglou, A. Floros, P. L. Dragotti, T. Stathaki

TBA

CueCo: Contrastive Clustering for Unsupervised Representation Learning

N. Giakoumoglou, T. Stathaki

TBA