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Learning representations from unlabeled data using pretext tasks and contrastive methods
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Basé sur la classification professionnelle SOC
Building autonomous AI agents capable of reasoning, planning, and executing multi-step tasks
Learning from a small number of examples per class using metric learning and meta-learning
Techniques and frameworks for generating new data instances that match the distribution of training data
Advanced techniques for training and fine-tuning transformer-based language models at scale
Foundational understanding and practical implementation of transformer-based language models
Integrating and reasoning across multiple data modalities including text, images, audio, and video
| name | Self-Supervised Learning |
| category | ai |
| description | Learning representations from unlabeled data using pretext tasks and contrastive methods |
I enable machines to learn useful representations from large amounts of unlabeled data by creating supervised signals from the data itself. I eliminate the dependency on human-annotated labels by designing pretext tasks that encourage models to learn meaningful features. The learned representations can then be transferred to downstream tasks with minimal fine-tuning.
Pretext Tasks: Artificially created supervised tasks that don't require labels but teach useful representations.
Contrastive Learning: Learning by pulling similar examples together and pushing dissimilar examples apart in embedding space.
Momentum Contrast (MoCo): Using a momentum queue to maintain consistent negative samples during contrastive learning.
SimCLR Framework: Simple contrastive learning framework with data augmentation and large batch sizes.
Masked Prediction: Learning by predicting masked portions of input, used successfully in BERT and MAE.
BYOL and Bootstrap Your Own Latent: Methods that avoid negative samples through online and target networks.
Cluster Assignment: Methods like SwAV that assign cluster predictions as supervision signal.
Information Maximization: Learning representations that maximize mutual information between views.
import torch
import torch.nn as nn
import torch.nn.functional as F
class SimCLRProjectionHead(nn.Module):
def __init__(self, input_dim=2048, hidden_dim=2048, output_dim=128):
super().__init__()
self.head = nn.Sequential(
nn.Linear(input_dim, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, output_dim)
)
def forward(self, features):
return self.head(features)
def nt_xent_loss(z_i, z_j, temperature=0.5):
batch_size = z_i.size(0)
z = torch.cat([z_i, z_j], dim=0)
similarity = torch.matmul(z, z.t()) / temperature
mask = torch.eye(2 * batch_size, device=z.device)
similarity = similarity.masked_fill(mask.bool(), -float('inf'))
pos_mask = torch.zeros(2 * batch_size, 2 * batch_size, device=z.device)
pos_mask[:batch_size, batch_size:] = torch.eye(batch_size, device=z.device)
pos_mask[batch_size:, :batch_size] = torch.eye(batch_size, device=z.device)
loss_i = -torch.log(
torch.exp(similarity[:batch_size, batch_size:2*batch_size].diag()) /
torch.exp(similarity[:batch_size]).sum(dim=1)
)
loss_j = -torch.log(
torch.exp(similarity[batch_size:, :batch_size].diag()) /
torch.exp(similarity[batch_size:]).sum(dim=1)
)
return (loss_i + loss_j).mean()
import torch
import torch.nn as nn
import torch.nn.functional as F
class MoCoQueue:
def __init__(self, queue_size=65536, embed_dim=128, momentum=0.999):
self.queue_size = queue_size
self.embed_dim = embed_dim
self.momentum = momentum
self.register_buffer("queue", torch.randn(queue_size, embed_dim))
self.queue = F.normalize(self.queue, p=2, dim=1)
self.register_buffer("queue_ptr", torch.zeros(1, dtype=torch.long))
@torch.no_grad()
def update(self, keys):
batch_size = keys.size(0)
ptr = int(self.queue_ptr)
self.queue[ptr:ptr+batch_size] = keys
ptr = (ptr + batch_size) % self.queue_size
self.queue_ptr[0] = ptr
def forward(self, query, key, momentum_encoder):
query = F.normalize(query, p=2, dim=1)
with torch.no_grad():
key = momentum_encoder(key)
key = F.normalize(key, p=2, dim=1)
self.update(key)
positive = torch.sum(query * key, dim=-1, keepdim=True)
negative = torch.matmul(query, self.queue.t())
logits = torch.cat([positive, negative], dim=1) / 0.07
labels = torch.zeros(logits.size(0), dtype=torch.long, device=query.device)
return logits, labels
class MoCoModel(nn.Module):
def __init__(self, base_encoder, embed_dim=128, m=0.999):
super().__init__()
self.encoder_q = base_encoder
self.encoder_k = base_encoder
self.projector_q = SimCLRProjectionHead(output_dim=embed_dim)
self.projector_k = SimCLRProjectionHead(output_dim=embed_dim)
for param_q, param_k in zip(self.encoder_q.parameters(), self.encoder_k.parameters()):
param_k.data.copy_(param_q.data)
param_k.requires_grad = False
self.moco_queue = MoCoQueue(embed_dim=embed_dim, momentum=m)
def forward(self, im_q, im_k):
q = self.encoder_q(im_q)
q = self.projector_q(q)
logits, labels = self.moco_queue.forward(q, im_k, self.encoder_k)
return logits, labels
@torch.no_grad()
def _momentum_update_key_encoder(self):
for param_q, param_k in zip(self.encoder_q.parameters(), self.encoder_k.parameters()):
param_k.data = param_k.data * self.m + param_q.data * (1. - self.m)
import torch
import torch.nn as nn
import torch.nn.functional as F
class BYOLPredictor(nn.Module):
def __init__(self, input_dim=2048, hidden_dim=512, output_dim=128):
super().__init__()
self.predictor = nn.Sequential(
nn.Linear(input_dim, hidden_dim),
nn.BatchNorm1d(hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, output_dim)
)
def forward(self, z):
return self.predictor(z)
class BYOLModel(nn.Module):
def __init__(self, base_encoder, input_dim=2048, hidden_dim=512, output_dim=128, m=0.996):
super().__init__()
self.encoder = base_encoder
self.projector = nn.Sequential(
nn.Linear(input_dim, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, output_dim)
)
self.predictor = BYOLPredictor(input_dim, hidden_dim, output_dim)
self.online_encoder = nn.Sequential(self.encoder, self.projector)
self.target_encoder = nn.Sequential(
type(self.encoder)(**self.encoder_kwargs),
type(self.projector)(**self.projector_kwargs)
)
for param in self.target_encoder.parameters():
param.requires_grad = False
self.m = m
def forward(self, v1, v2):
h1 = self.online_encoder(v1)
h2 = self.online_encoder(v2)
z1 = F.normalize(h1, dim=1)
z2 = F.normalize(h2, dim=1)
p1 = self.predictor(z1)
p2 = self.predictor(z2)
with torch.no_grad():
t1 = self.target_encoder(v1)
t2 = self.target_encoder(v2)
z1_t = F.normalize(t1, dim=1)
z2_t = F.normalize(t2, dim=1)
loss = 2 - F.cosine_similarity(p1, z2_t, dim=-1).mean() - F.cosine_similarity(p2, z1_t, dim=-1).mean()
return loss
@torch.no_grad()
def update_target(self):
for param_o, param_t in zip(self.online_encoder.parameters(), self.target_encoder.parameters()):
param_t.data = param_t.data * self.m + param_o.data * (1 - self.m)
import torch
import random
from typing import Tuple, List
class SimCLRTransforms:
def __init__(self, size=224):
self.color_jitter = torch.nn.ColorJitter(
brightness=0.4, contrast=0.4, saturation=0.4, hue=0.1
)
self.gaussian_blur = GaussianBlur(kernel_size=23)
self.size = size
def __call__(self, img):
t1 = self._transform(img)
t2 = self._transform(img)
return t1, t2
def _transform(self, img):
img = self._random_resize_crop(img)
img = self.color_jitter(img)
img = self._random_grayscale(img)
if random.random() < 0.5:
img = self.gaussian_blur(img)
img = self._normalize(img)
return img
def _random_resize_crop(self, img):
i, j, h, w = transforms.RandomResizedCrop.get_params(img, (0.08, 1.0), (3/4, 4/3))
return transforms.functional.resized_crop(img, i, j, h, w, self.size)
def _random_grayscale(self, img):
if random.random() < 0.2:
return transforms.functional.to_grayscale(img)
return img
def _normalize(self, img):
return transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])(img)
class GaussianBlur:
def __init__(self, kernel_size=23):
self.kernel_size = kernel_size
def __call__(self, img):
sigma = random.uniform(0.1, 2.0)
return transforms.functional.gaussian_blur(img, self.kernel_size, sigma=[sigma])
import torch
import torch.nn as nn
class MAEEncoder(nn.Module):
def __init__(self, patch_size=16, embed_dim=768, depth=12, num_heads=12):
super().__init__()
self.patch_embed = nn.Conv2d(3, embed_dim, kernel_size=patch_size, stride=patch_size)
self.pos_embed = nn.Parameter(torch.zeros(1, 196, embed_dim))
self.blocks = nn.ModuleList([
nn.TransformerEncoderLayer(
d_model=embed_dim,
nhead=num_heads,
dim_feedforward=4*embed_dim,
dropout=0.0
) for _ in range(depth)
])
self.norm = nn.LayerNorm(embed_dim)
self._init_weights()
def _init_weights(self):
nn.init.trunc_normal_(self.pos_embed, std=0.02)
nn.init.xavier_uniform_(self.patch_embed.weight)
def forward(self, x):
x = self.patch_embed(x)
x = x.flatten(2).transpose(1, 2)
x = x + self.pos_embed
for block in self.blocks:
x = block(x)
return self.norm(x)
class MAEDecoder(nn.Module):
def __init__(self, embed_dim=768, depth=8, num_heads=16, patch_size=16, num_patches=196):
super().__init__()
self.mask_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
self.blocks = nn.ModuleList([
nn.TransformerEncoderLayer(
d_model=embed_dim,
nhead=num_heads,
dim_feedforward=4*embed_dim
) for _ in range(depth)
])
self.head = nn.Linear(embed_dim, 3 * patch_size ** 2)
self._init_weights()
def _init_weights(self):
nn.init.normal_(self.mask_token, std=0.02)
nn.init.trunc_normal_(self.pos_embed, std=0.02)
def forward(self, x, ids_restore, ids_keep):
x = self._unpatchify(x)
return x
def _unpatchify(self, x):
C = 3
p = 16
h = w = int(x.shape[1] ** 0.5)
x = x.reshape(shape=(x.shape[0], h, w, p, p, C))
x = torch.einsum('nhwpqc->nchpwq', x)
return x.reshape(x.shape[0], C, h * p, w * p)
def mae_loss(reconstructed, original, mask):
loss = (reconstructed - original) ** 2
loss = loss.mean(dim=-1)
loss = (loss * mask).sum() / mask.sum()
return loss
Use stronger augmentations for contrastive learning to create more informative positive pairs.
Increase batch size for SimCLR as it directly affects the number of negative samples.
Use learning rate warmup and cosine scheduling for stable contrastive training.
Monitor representation similarity metrics to track learning progress.
Use projection heads to map features to contrastive space, not raw backbone outputs.
Apply symmetric loss terms when using two views to ensure balanced optimization.
Use momentum encoders to maintain consistency in MoCo and BYOL.
Choose appropriate temperature values (typically 0.1) for contrastive losses.
Use sufficient epochs for pre-training as contrastive learning converges slower than supervised.
Evaluate using linear probing on frozen features to assess representation quality.