ruvector/docs/research/cnn/SOTA_OVERVIEW.md

CNN State-of-the-Art: Contrastive Learning Overview

Executive Summary

This document surveys the current state-of-the-art in contrastive learning for CNNs, focusing on self-supervised methods that can generate high-quality embeddings without labeled data. These techniques are particularly relevant for RuVector's vector similarity search capabilities.

Key Contrastive Learning Frameworks

1. SimCLR (Simple Contrastive Learning of Visual Representations)

Paper: A Simple Framework for Contrastive Learning of Visual Representations (Google, 2020)

Architecture:

Image -> Data Augmentation (2 views) -> CNN Encoder -> Projection Head -> Contrastive Loss

Key Innovations:

• Composition of data augmentations is critical for effective learning
• Learnable nonlinear projection head between representation and loss
• Benefits from larger batch sizes (4096-8192) and longer training

Performance: 76.5% top-1 accuracy on ImageNet (linear evaluation)

Limitations:

• Requires large batch sizes for sufficient negative samples
• Computationally expensive (many GPU hours)

2. MoCo (Momentum Contrast)

Paper: Momentum Contrast for Unsupervised Visual Representation Learning (Meta AI)

Key Innovation: Dynamic memory queue decouples batch size from number of negatives

Architecture:

Query Encoder -> Current batch features
Momentum Encoder -> Memory Queue (65536 negatives)

Advantages over SimCLR:

• Works with smaller batch sizes (256)
• Memory-efficient via queue mechanism
• Momentum update prevents encoder collapse

MoCo v3 Performance: Approaches supervised learning baselines

3. BYOL (Bootstrap Your Own Latent)

Paper: Bootstrap Your Own Latent (DeepMind)

Key Innovation: Eliminates negative samples entirely

Architecture:

Online Network: Encoder -> Projector -> Predictor
Target Network: Encoder -> Projector (momentum updated)

Loss: L2 distance between online prediction and target projection

Advantages:

• No need for large batch sizes
• No negative sampling required
• Avoids false negative problem
• More stable training

Considerations: Requires careful architecture design to prevent collapse

4. SwAV (Swapped Assignments between Views)

Paper: Unsupervised Learning of Visual Features by Contrasting Cluster Assignments

Key Innovation: Clustering-based approach with swapped assignments

Architecture:

Image Views -> Encoder -> Prototype Assignment
Swapped Prediction: View1 predicts View2's cluster, vice versa

Performance: 75.30% top-1 ImageNet (outperforms direct comparison methods)

Advantages:

• No large batch or memory bank needed
• Multi-crop augmentation strategy
• Online clustering is efficient

Contrastive Loss Functions

InfoNCE / NT-Xent Loss

The Normalized Temperature-scaled Cross Entropy (NT-Xent) loss, also known as InfoNCE, is the standard loss for contrastive learning:

L = -log(exp(sim(z_i, z_j)/τ) / Σ_k exp(sim(z_i, z_k)/τ))

Components:

• sim(z_i, z_j): Similarity function (cosine or dot product)
• τ: Temperature parameter (typically 0.1-0.5)
• Numerator: Positive pair similarity
• Denominator: Sum over all pairs (1 positive + N-1 negatives)

Temperature Effects:

• Lower τ (0.1): Sharper distributions, focus on hard negatives
• Higher τ (0.5): Smoother distributions, more uniform gradients

Implementation:

fn info_nce_loss(embeddings: &[Vec<f32>], temperature: f32) -> f32 {
    // For each positive pair (i, j):
    // 1. Compute cosine similarity between all pairs
    // 2. Apply temperature scaling
    // 3. Compute softmax cross-entropy
    // 4. Average over batch
}

Triplet Loss

Classic metric learning loss:

L = max(0, d(a, p) - d(a, n) + margin)

Components:

• Anchor (a), Positive (p), Negative (n) triplets
• Margin (typically 0.2-1.0)

Hard Negative Mining: Critical for training efficiency

Use Cases: Face verification (FaceNet), fine-grained similarity

Comparison Table

Loss	Negatives Required	Batch Size	Training Stability	Best For
NT-Xent	Yes (in-batch)	Large (4096+)	High	Self-supervised pretraining
InfoNCE	Yes	Medium-Large	High	General contrastive
Triplet	Yes (mined)	Any	Moderate	Metric learning
BYOL Loss	No	Any	Requires care	No-negative scenarios

2024-2025 State-of-the-Art Performance

ImageNet Benchmarks

Method	Architecture	Top-1 Accuracy	Notes
MAE ViT-Huge	ViT-H	87.8%	Masked autoencoder (generative)
ReLICv2	ResNet-50	77.1%	Contrastive
SwAV	ResNet-50	75.3%	Clustering-based
SimCLR	ResNet-50	76.5%	Requires large batch
MoCo v3	ViT-B	76.7%	Momentum contrast
BYOL	ResNet-50	74.3%	No negatives

Key Insights from Recent Research

1. Data augmentation strategy matters more than SSL paradigm
2. Vision Transformers benefit significantly from SSL pre-training
3. Masked image modeling provides simplicity and efficiency benefits
4. Scaling to larger models and datasets improves performance
5. SSL has become de-facto standard for ImageNet pre-training

Efficient Architectures for Embedding Extraction

MobileNet Series

MobileNet-V2/V3: Depthwise separable convolutions

Standard Conv: H×W×C_in×K×K×C_out multiplications
Depthwise Separable: H×W×C_in×K×K + H×W×C_in×C_out
Reduction: ~8-9x fewer operations for 3×3 kernels

Performance: 98.15% accuracy on activity recognition (6MB model)

ShuffleNet

Key Innovation: Channel shuffle after group convolution

FLOPs: 10-150 MFLOPs range

Performance: Surpasses MobileNet by 7.8% at ~40 MFLOPs

EfficientNet

Compound Scaling: Balanced depth, width, resolution scaling

Trade-off: Higher accuracy but more parameters/MACs

Best for: When compute budget is flexible

Practical Recommendations for RuVector

Architecture Selection

Use Case	Recommended Architecture	Notes
Real-time embedding	MobileNet-V3 Small	~3ms inference
Balanced accuracy/speed	ShuffleNet-V2	Good SIMD compatibility
Maximum quality	EfficientNet-B0	INT8 quantizable

Pre-training Strategy

1. For domain-specific data:

   • Use SimCLR/MoCo with domain augmentations
   • Fine-tune projection head on similarity task

2. For general embeddings:

   • Start with ImageNet pre-trained weights
   • Extract features from penultimate layer

3. For CPU deployment:

   • MobileNet-V3 with INT8 quantization
   • Winograd convolution for 3x3 kernels

Embedding Dimensions

Dimension	Use Case	Trade-off
128-256	Real-time search	Fast, compact
512	Balanced	Good quality/size
1024-2048	Maximum recall	Higher memory

Integration with Vector Search

FAISS Integration Pattern

// 1. CNN extracts embedding
let embedding = cnn.forward(image); // [batch, 512]

// 2. L2 normalize for cosine similarity
let normalized = l2_normalize(embedding);

// 3. Add to FAISS index or RuVector
index.add(normalized);

// 4. Search uses inner product (= cosine after normalization)
let results = index.search(query, k);

Recommended Index Types

Dataset Size	Index Type	Build Time	Search Time
<10K	Flat	O(n)	O(n)
10K-1M	IVF	O(n)	O(sqrt(n))
>1M	HNSW	O(n log n)	O(log n)

References

1. SimCLR Paper
2. Contrastive Learning Overview
3. LearnOpenCV: SimCLR and BYOL
4. Self-Supervised Learning Survey
5. InfoNCE PyTorch
6. PyTorch Metric Learning Losses
7. FAISS Library
8. ShuffleNet Paper